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From prognostics and health systems management to predictive maintenance . 2 Knowledge, reliability and decision / / Brigitte Chebel-Morello, Jean-Marc Nicod, Christophe Varnier
| From prognostics and health systems management to predictive maintenance . 2 Knowledge, reliability and decision / / Brigitte Chebel-Morello, Jean-Marc Nicod, Christophe Varnier |
| Autore | Chebel-Morello Brigitte |
| Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (157 pages) : illustrations (some color) |
| Disciplina | 621.3810288 |
| Collana | Mechanical Engineering and Solid Mechanics Series |
| Soggetto topico |
Electronic systems - Maintenance and repair
Electronic systems - Testing |
| ISBN |
1-119-43685-0
1-119-43686-9 1-119-43680-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910271011703321 |
Chebel-Morello Brigitte
|
||
| London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
From prognostics and health systems management to predictive maintenance . 2 Knowledge, reliability and decision / / Brigitte Chebel-Morello, Jean-Marc Nicod, Christophe Varnier
| From prognostics and health systems management to predictive maintenance . 2 Knowledge, reliability and decision / / Brigitte Chebel-Morello, Jean-Marc Nicod, Christophe Varnier |
| Autore | Chebel-Morello Brigitte |
| Pubbl/distr/stampa | London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (157 pages) : illustrations (some color) |
| Disciplina | 621.3810288 |
| Collana | Mechanical Engineering and Solid Mechanics Series |
| Soggetto topico |
Electronic systems - Maintenance and repair
Electronic systems - Testing |
| ISBN |
1-119-43685-0
1-119-43686-9 1-119-43680-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910818212303321 |
|
Chebel-Morello Brigitte
|
||
| London, England ; ; Hoboken, New Jersey : , : ISTE : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
International journal of prognostics and health management
| International journal of prognostics and health management |
| Pubbl/distr/stampa | [Rochester, NY], : Prognostics and Health Management Society |
| Descrizione fisica | 1 online resource |
| Disciplina | 621.381 |
| Soggetto topico |
Electronic systems - Maintenance and repair
Electronic systems - Testing System failures (Engineering) - Prevention |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Electrical Engineering |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | IJPHM |
| Record Nr. | UNINA-9910141443603321 |
| [Rochester, NY], : Prognostics and Health Management Society | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
International journal of prognostics and health management
| International journal of prognostics and health management |
| Pubbl/distr/stampa | [Rochester, NY], : Prognostics and Health Management Society |
| Descrizione fisica | 1 online resource |
| Disciplina | 621.381 |
| Soggetto topico |
Electronic systems - Maintenance and repair
Electronic systems - Testing System failures (Engineering) - Prevention |
| Soggetto genere / forma | Periodicals. |
| Soggetto non controllato | Electrical Engineering |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | IJPHM |
| Record Nr. | UNISA-996321619003316 |
| [Rochester, NY], : Prognostics and Health Management Society | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Prognostics and health management of electronics [[electronic resource] /] / Michael G. Pecht
| Prognostics and health management of electronics [[electronic resource] /] / Michael G. Pecht |
| Autore | Pecht Michael |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (335 p.) |
| Disciplina |
621.381
621.381028/8 |
| Soggetto topico | Electronic systems - Maintenance and repair |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-81459-8
9786611814595 0-470-38584-7 0-470-38583-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Prognostics and Health Management of Electronics; Contents; Preface; Acknowledgements; Acronyms; Chapter 1 Introduction; 1.1 Reliability and Prognostics; 1.2 PHM for Electronics; 1.3 PHM Concepts and Methods; 1.3.1 Fuses and Canaries; 1.3.2 Monitoring and Reasoning of Failure Precursors; 1.3.3 Monitoring Environmental and Usage Profiles for Damage Modeling; 1.4 Implementation of PHM for System of Systems; 1.5 Summary; Chapter 2 Sensor Systems for PHM; 2.1 Sensor and Sensing Principles; 2.1.1 Thermal Sensors; 2.1.2 Electrical Sensors; 2.1.3 Mechanical Sensors; 2.1.4 Humidity Sensors
2.1.5 Biosensors2.1.6 Chemical Sensors; 2.1.7 Optical Sensors; 2.1.8 Magnetic Sensors; 2.2 Sensor Systems for PHM; 2.2.1 Parameters to Be Monitored; 2.2.2 Sensor System Performance; 2.2.3 Physical Attributes of Sensor Systems; 2.2.4 Functional Attributes of Sensor Systems; 2.2.5 Cost; 2.2.6 Reliability; 2.2.7 Availability; 2.3 Sensor Selection; 2.4 Examples of Sensor Systems for PHM Implementation; 2.5 Emerging Trends in Sensor Technology for PHM; Chapter 3 Data-Driven Approaches for PHM; 3.1 Introduction; 3.2 Parametric Statistical Methods; 3.2.1 Likelihood Ratio Test 3.2.2 Maximum Likelihood Estimation3.2.3 Neyman-Pearson Criterion; 3.2.4 Expectation Maximization; 3.2.5 Minimum Mean Square Error Estimation; 3.2.6 Maximum A Posteriori Estimation; 3.2.7 Rao-Blackwell Estimation; 3.2.8 Cramer-Rao Lower Bound; 3.3 Nonparametric Statistical Methods; 3.3.1 Nearest Neighbor-Based Classification; 3.3.2 Parzen Window (or Kernel Density Estimation); 3.3.3 Wilcoxon Rank-Sum Test; 3.3.4 Kolmogorov-Smirnov Test; 3.3.5 Chi Square Test; 3.4 Machine Learning Techniques; 3.5 Supervised Classification; 3.5.1 Discriminative Approach; 3.5.2 Generative Approach 3.6 Unsupervised Classification3.6.1 Discriminative Approach; 3.6.2 Generative Approach; 3.7 Summary; Chapter 4 Physics-of-Failure Approach to PHM; 4.1 PoF-Based PHM Methodology; 4.2 Hardware Configuration; 4.3 Loads; 4.4 Failure Modes, Mechanisms, and Effects Analysis; 4.5 Stress Analysis; 4.6 Reliability Assessment and Remaining-Life Predictions; 4.7 Outputs from PoF-Based PHM; Chapter 5 The Economics of PHM; 5.1 Return on Investment; 5.1.1 PHM ROI Analyses; 5.1.2 Financial Costs; 5.2 PHM Cost-Modeling Terminology and Definitions; 5.3 PHM Implementation Costs; 5.3.1 Nonrecurring Costs 5.3.2 Recurring Costs5.3.3 Infrastructure Costs; 5.3.4 Nonmonetary Considerations and Maintenance Culture; 5.4 Cost Avoidance; 5.4.1 Maintenance Planning Cost Avoidance; 5.4.2 Discrete Event Simulation Maintenance Planning Model; 5.4.3 Fixed-Schedule Maintenance Interval; 5.4.4 Precursor to Failure Monitoring; 5.4.5 LRU-Independent Methods; 5.4.6 Discrete Event Simulation Implementation Details; 5.4.7 Operational Profile; 5.5 Example PHM Cost Analysis; 5.5.1 Single-Socket Model Results; 5.5.2 Multiple-Socket Model Results; 5.5.3 Example Business Case Construction; 5.6 Summary Chapter 6 PHM Roadmap: Challenges and Opportunities |
| Record Nr. | UNINA-9910144128903321 |
|
Pecht Michael
|
||
| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Prognostics and health management of electronics [[electronic resource] /] / Michael G. Pecht
| Prognostics and health management of electronics [[electronic resource] /] / Michael G. Pecht |
| Autore | Pecht Michael |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (335 p.) |
| Disciplina |
621.381
621.381028/8 |
| Soggetto topico | Electronic systems - Maintenance and repair |
| ISBN |
1-281-81459-8
9786611814595 0-470-38584-7 0-470-38583-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Prognostics and Health Management of Electronics; Contents; Preface; Acknowledgements; Acronyms; Chapter 1 Introduction; 1.1 Reliability and Prognostics; 1.2 PHM for Electronics; 1.3 PHM Concepts and Methods; 1.3.1 Fuses and Canaries; 1.3.2 Monitoring and Reasoning of Failure Precursors; 1.3.3 Monitoring Environmental and Usage Profiles for Damage Modeling; 1.4 Implementation of PHM for System of Systems; 1.5 Summary; Chapter 2 Sensor Systems for PHM; 2.1 Sensor and Sensing Principles; 2.1.1 Thermal Sensors; 2.1.2 Electrical Sensors; 2.1.3 Mechanical Sensors; 2.1.4 Humidity Sensors
2.1.5 Biosensors2.1.6 Chemical Sensors; 2.1.7 Optical Sensors; 2.1.8 Magnetic Sensors; 2.2 Sensor Systems for PHM; 2.2.1 Parameters to Be Monitored; 2.2.2 Sensor System Performance; 2.2.3 Physical Attributes of Sensor Systems; 2.2.4 Functional Attributes of Sensor Systems; 2.2.5 Cost; 2.2.6 Reliability; 2.2.7 Availability; 2.3 Sensor Selection; 2.4 Examples of Sensor Systems for PHM Implementation; 2.5 Emerging Trends in Sensor Technology for PHM; Chapter 3 Data-Driven Approaches for PHM; 3.1 Introduction; 3.2 Parametric Statistical Methods; 3.2.1 Likelihood Ratio Test 3.2.2 Maximum Likelihood Estimation3.2.3 Neyman-Pearson Criterion; 3.2.4 Expectation Maximization; 3.2.5 Minimum Mean Square Error Estimation; 3.2.6 Maximum A Posteriori Estimation; 3.2.7 Rao-Blackwell Estimation; 3.2.8 Cramer-Rao Lower Bound; 3.3 Nonparametric Statistical Methods; 3.3.1 Nearest Neighbor-Based Classification; 3.3.2 Parzen Window (or Kernel Density Estimation); 3.3.3 Wilcoxon Rank-Sum Test; 3.3.4 Kolmogorov-Smirnov Test; 3.3.5 Chi Square Test; 3.4 Machine Learning Techniques; 3.5 Supervised Classification; 3.5.1 Discriminative Approach; 3.5.2 Generative Approach 3.6 Unsupervised Classification3.6.1 Discriminative Approach; 3.6.2 Generative Approach; 3.7 Summary; Chapter 4 Physics-of-Failure Approach to PHM; 4.1 PoF-Based PHM Methodology; 4.2 Hardware Configuration; 4.3 Loads; 4.4 Failure Modes, Mechanisms, and Effects Analysis; 4.5 Stress Analysis; 4.6 Reliability Assessment and Remaining-Life Predictions; 4.7 Outputs from PoF-Based PHM; Chapter 5 The Economics of PHM; 5.1 Return on Investment; 5.1.1 PHM ROI Analyses; 5.1.2 Financial Costs; 5.2 PHM Cost-Modeling Terminology and Definitions; 5.3 PHM Implementation Costs; 5.3.1 Nonrecurring Costs 5.3.2 Recurring Costs5.3.3 Infrastructure Costs; 5.3.4 Nonmonetary Considerations and Maintenance Culture; 5.4 Cost Avoidance; 5.4.1 Maintenance Planning Cost Avoidance; 5.4.2 Discrete Event Simulation Maintenance Planning Model; 5.4.3 Fixed-Schedule Maintenance Interval; 5.4.4 Precursor to Failure Monitoring; 5.4.5 LRU-Independent Methods; 5.4.6 Discrete Event Simulation Implementation Details; 5.4.7 Operational Profile; 5.5 Example PHM Cost Analysis; 5.5.1 Single-Socket Model Results; 5.5.2 Multiple-Socket Model Results; 5.5.3 Example Business Case Construction; 5.6 Summary Chapter 6 PHM Roadmap: Challenges and Opportunities |
| Record Nr. | UNINA-9910830237203321 |
|
Pecht Michael
|
||
| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Prognostics and health management of electronics [[electronic resource] /] / Michael G. Pecht
| Prognostics and health management of electronics [[electronic resource] /] / Michael G. Pecht |
| Autore | Pecht Michael |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, c2008 |
| Descrizione fisica | 1 online resource (335 p.) |
| Disciplina |
621.381
621.381028/8 |
| Soggetto topico | Electronic systems - Maintenance and repair |
| ISBN |
1-281-81459-8
9786611814595 0-470-38584-7 0-470-38583-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Prognostics and Health Management of Electronics; Contents; Preface; Acknowledgements; Acronyms; Chapter 1 Introduction; 1.1 Reliability and Prognostics; 1.2 PHM for Electronics; 1.3 PHM Concepts and Methods; 1.3.1 Fuses and Canaries; 1.3.2 Monitoring and Reasoning of Failure Precursors; 1.3.3 Monitoring Environmental and Usage Profiles for Damage Modeling; 1.4 Implementation of PHM for System of Systems; 1.5 Summary; Chapter 2 Sensor Systems for PHM; 2.1 Sensor and Sensing Principles; 2.1.1 Thermal Sensors; 2.1.2 Electrical Sensors; 2.1.3 Mechanical Sensors; 2.1.4 Humidity Sensors
2.1.5 Biosensors2.1.6 Chemical Sensors; 2.1.7 Optical Sensors; 2.1.8 Magnetic Sensors; 2.2 Sensor Systems for PHM; 2.2.1 Parameters to Be Monitored; 2.2.2 Sensor System Performance; 2.2.3 Physical Attributes of Sensor Systems; 2.2.4 Functional Attributes of Sensor Systems; 2.2.5 Cost; 2.2.6 Reliability; 2.2.7 Availability; 2.3 Sensor Selection; 2.4 Examples of Sensor Systems for PHM Implementation; 2.5 Emerging Trends in Sensor Technology for PHM; Chapter 3 Data-Driven Approaches for PHM; 3.1 Introduction; 3.2 Parametric Statistical Methods; 3.2.1 Likelihood Ratio Test 3.2.2 Maximum Likelihood Estimation3.2.3 Neyman-Pearson Criterion; 3.2.4 Expectation Maximization; 3.2.5 Minimum Mean Square Error Estimation; 3.2.6 Maximum A Posteriori Estimation; 3.2.7 Rao-Blackwell Estimation; 3.2.8 Cramer-Rao Lower Bound; 3.3 Nonparametric Statistical Methods; 3.3.1 Nearest Neighbor-Based Classification; 3.3.2 Parzen Window (or Kernel Density Estimation); 3.3.3 Wilcoxon Rank-Sum Test; 3.3.4 Kolmogorov-Smirnov Test; 3.3.5 Chi Square Test; 3.4 Machine Learning Techniques; 3.5 Supervised Classification; 3.5.1 Discriminative Approach; 3.5.2 Generative Approach 3.6 Unsupervised Classification3.6.1 Discriminative Approach; 3.6.2 Generative Approach; 3.7 Summary; Chapter 4 Physics-of-Failure Approach to PHM; 4.1 PoF-Based PHM Methodology; 4.2 Hardware Configuration; 4.3 Loads; 4.4 Failure Modes, Mechanisms, and Effects Analysis; 4.5 Stress Analysis; 4.6 Reliability Assessment and Remaining-Life Predictions; 4.7 Outputs from PoF-Based PHM; Chapter 5 The Economics of PHM; 5.1 Return on Investment; 5.1.1 PHM ROI Analyses; 5.1.2 Financial Costs; 5.2 PHM Cost-Modeling Terminology and Definitions; 5.3 PHM Implementation Costs; 5.3.1 Nonrecurring Costs 5.3.2 Recurring Costs5.3.3 Infrastructure Costs; 5.3.4 Nonmonetary Considerations and Maintenance Culture; 5.4 Cost Avoidance; 5.4.1 Maintenance Planning Cost Avoidance; 5.4.2 Discrete Event Simulation Maintenance Planning Model; 5.4.3 Fixed-Schedule Maintenance Interval; 5.4.4 Precursor to Failure Monitoring; 5.4.5 LRU-Independent Methods; 5.4.6 Discrete Event Simulation Implementation Details; 5.4.7 Operational Profile; 5.5 Example PHM Cost Analysis; 5.5.1 Single-Socket Model Results; 5.5.2 Multiple-Socket Model Results; 5.5.3 Example Business Case Construction; 5.6 Summary Chapter 6 PHM Roadmap: Challenges and Opportunities |
| Record Nr. | UNINA-9910877123103321 |
|
Pecht Michael
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||
| Hoboken, N.J., : Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Prognostics and health management of electronics : fundamentals, machine learning, and internet of things / / edited by Michael Pecht, Ph.D., PE, Myeongsu Kang, Ph.D
| Prognostics and health management of electronics : fundamentals, machine learning, and internet of things / / edited by Michael Pecht, Ph.D., PE, Myeongsu Kang, Ph.D |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2018 |
| Descrizione fisica | 1 online resource (808 pages) |
| Disciplina | 621.381028/8 |
| Soggetto topico | Electronic systems - Maintenance and repair |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-51532-7
1-119-51530-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
List of Contributors xxiii -- Preface xxvii -- About the Contributors xxxv -- Acknowledgment xlvii -- List of Abbreviations xlix -- 1 Introduction to PHM 1 /Michael G. Pecht andMyeongsu Kang -- 1.1 Reliability and Prognostics 1 -- 1.2 PHM for Electronics 3 -- 1.3 PHM Approaches 6 -- 1.3.1 PoF-Based Approach 6 -- 1.3.1.1 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 7 -- 1.3.1.2 Life-Cycle Load Monitoring 8 -- 1.3.1.3 Data Reduction and Load Feature Extraction 10 -- 1.3.1.4 Data Assessment and Remaining Life Calculation 12 -- 1.3.1.5 Uncertainty Implementation and Assessment 13 -- 1.3.2 Canaries 14 -- 1.3.3 Data-Driven Approach 16 -- 1.3.3.1 Monitoring and Reasoning of Failure Precursors 16 -- 1.3.3.2 Data Analytics and Machine Learning 20 -- 1.3.4 Fusion Approach 23 -- 1.4 Implementation of PHM in a System of Systems 24 -- 1.5 PHM in the Internet ofThings (IoT) Era 26 -- 1.5.1 IoT-Enabled PHM Applications: Manufacturing 27 -- 1.5.2 IoT-Enabled PHM Applications: Energy Generation 27 -- 1.5.3 IoT-Enabled PHM Applications: Transportation and Logistics 28 -- 1.5.4 IoT-Enabled PHM Applications: Automobiles 28 -- 1.5.5 IoT-Enabled PHM Applications: Medical Consumer Products 29 -- 1.5.6 IoT-Enabled PHM Applications:Warranty Services 29 -- 1.5.7 IoT-Enabled PHM Applications: Robotics 30 -- 1.6 Summary 30 -- References 30 -- 2 Sensor Systems for PHM 39 /Hyunseok Oh,Michael H. Azarian, Shunfeng Cheng, andMichael G. Pecht -- 2.1 Sensor and Sensing Principles 39 -- 2.1.1 Thermal Sensors 40 -- 2.1.2 Electrical Sensors 41 -- 2.1.3 Mechanical Sensors 42 -- 2.1.4 Chemical Sensors 42 -- 2.1.5 Humidity Sensors 44 -- 2.1.6 Biosensors 44 -- 2.1.7 Optical Sensors 45 -- 2.1.8 Magnetic Sensors 45 -- 2.2 Sensor Systems for PHM 46 -- 2.2.1 Parameters to be Monitored 47 -- 2.2.2 Sensor System Performance 48 -- 2.2.3 Physical Attributes of Sensor Systems 48 -- 2.2.4 Functional Attributes of Sensor Systems 49 -- 2.2.4.1 Onboard Power and Power Management 49 -- 2.2.4.2 Onboard Memory and Memory Management 50.
2.2.4.3 Programmable SamplingMode and Sampling Rate 51 -- 2.2.4.4 Signal Processing Software 51 -- 2.2.4.5 Fast and Convenient Data Transmission 52 -- 2.2.5 Reliability 53 -- 2.2.6 Availability 53 -- 2.2.7 Cost 54 -- 2.3 Sensor Selection 54 -- 2.4 Examples of Sensor Systems for PHM Implementation 54 -- 2.5 Emerging Trends in Sensor Technology for PHM 59 -- References 60 -- 3 Physics-of-Failure Approach to PHM 61 /Shunfeng Cheng, Nagarajan Raghavan, Jie Gu, Sony Mathew, and Michael G. Pecht -- 3.1 PoF-Based PHM Methodology 61 -- 3.2 Hardware Configuration 62 -- 3.3 Loads 63 -- 3.4 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 64 -- 3.4.1 Examples of FMMEA for Electronic Devices 68 -- 3.5 Stress Analysis 71 -- 3.6 Reliability Assessment and Remaining-Life Predictions 73 -- 3.7 Outputs from PoF-Based PHM 77 -- 3.8 Caution and Concerns in the Use of PoF-Based PHM 78 -- 3.9 Combining PoF with Data-Driven Prognosis 80 -- References 81 -- 4 Machine Learning: Fundamentals 85 /Myeongsu Kang and Noel Jordan Jameson -- 4.1 Types of Machine Learning 85 -- 4.1.1 Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning 86 -- 4.1.2 Batch and Online Learning 88 -- 4.1.3 Instance-Based and Model-Based Learning 89 -- 4.2 Probability Theory in Machine Learning: Fundamentals 90 -- 4.2.1 Probability Space and Random Variables 91 -- 4.2.2 Distributions, Joint Distributions, and Marginal Distributions 91 -- 4.2.3 Conditional Distributions 91 -- 4.2.4 Independence 92 -- 4.2.5 Chain Rule and Bayes Rule 92 -- 4.3 Probability Mass Function and Probability Density Function 93 -- 4.3.1 Probability Mass Function 93 -- 4.3.2 Probability Density Function 93 -- 4.4 Mean, Variance, and Covariance Estimation 94 -- 4.4.1 Mean 94 -- 4.4.2 Variance 94 -- 4.4.3 Robust Covariance Estimation 95 -- 4.5 Probability Distributions 96 -- 4.5.1 Bernoulli Distribution 96 -- 4.5.2 Normal Distribution 96 -- 4.5.3 Uniform Distribution 97 -- 4.6 Maximum Likelihood and Maximum A Posteriori Estimation 97. 4.6.1 Maximum Likelihood Estimation 97 -- 4.6.2 Maximum A Posteriori Estimation 98 -- 4.7 Correlation and Causation 99 -- 4.8 Kernel Trick 100 -- 4.9 Performance Metrics 102 -- 4.9.1 Diagnostic Metrics 102 -- 4.9.2 Prognostic Metrics 105 -- References 107 -- 5 Machine Learning: Data Pre-processing 111 /Myeongsu Kang and Jing Tian -- 5.1 Data Cleaning 111 -- 5.1.1 Missing Data Handling 111 -- 5.1.1.1 Single-Value Imputation Methods 113 -- 5.1.1.2 Model-Based Methods 113 -- 5.2 Feature Scaling 114 -- 5.3 Feature Engineering 116 -- 5.3.1 Feature Extraction 116 -- 5.3.1.1 PCA and Kernel PCA 116 -- 5.3.1.2 LDA and Kernel LDA 118 -- 5.3.1.3 Isomap 119 -- 5.3.1.4 Self-Organizing Map (SOM) 120 -- 5.3.2 Feature Selection 121 -- 5.3.2.1 Feature Selection: FilterMethods 122 -- 5.3.2.2 Feature Selection:WrapperMethods 124 -- 5.3.2.3 Feature Selection: Embedded Methods 124 -- 5.3.2.4 Advanced Feature Selection 125 -- 5.4 Imbalanced Data Handling 125 -- 5.4.1 SamplingMethods for Imbalanced Learning 126 -- 5.4.1.1 Synthetic Minority Oversampling Technique 126 -- 5.4.1.2 Adaptive Synthetic Sampling 126 -- 5.4.1.3 Effect of SamplingMethods for Diagnosis 127 -- References 129 -- 6 Machine Learning: Anomaly Detection 131 /Myeongsu Kang -- 6.1 Introduction 131 -- 6.2 Types of Anomalies 133 -- 6.2.1 Point Anomalies 134 -- 6.2.2 Contextual Anomalies 134 -- 6.2.3 Collective Anomalies 135 -- 6.3 Distance-Based Methods 136 -- 6.3.1 MD Calculation Using an Inverse Matrix Method 137 -- 6.3.2 MD Calculation Using a Gram-Schmidt Orthogonalization Method 137 -- 6.3.3 Decision Rules 138 -- 6.3.3.1 Gamma Distribution:Threshold Selection 138 -- 6.3.3.2 Weibull Distribution:Threshold Selection 139 -- 6.3.3.3 Box-Cox Transformation:Threshold Selection 139 -- 6.4 Clustering-Based Methods 140 -- 6.4.1 k-Means Clustering 141 -- 6.4.2 Fuzzy c-Means Clustering 142 -- 6.4.3 Self-Organizing Maps (SOMs) 142 -- 6.5 Classification-Based Methods 144 -- 6.5.1 One-Class Classification 145 -- 6.5.1.1 One-Class Support Vector Machines 145. 6.5.1.2 k-Nearest Neighbors 148 -- 6.5.2 Multi-Class Classification 149 -- 6.5.2.1 Multi-Class Support Vector Machines 149 -- 6.5.2.2 Neural Networks 151 -- 6.6 StatisticalMethods 153 -- 6.6.1 Sequential Probability Ratio Test 154 -- 6.6.2 Correlation Analysis 156 -- 6.7 Anomaly Detection with No System Health Profile 156 -- 6.8 Challenges in Anomaly Detection 158 -- References 159 -- 7 Machine Learning: Diagnostics and Prognostics 163 /Myeongsu Kang -- 7.1 Overview of Diagnosis and Prognosis 163 -- 7.2 Techniques for Diagnostics 165 -- 7.2.1 Supervised Machine Learning Algorithms 165 -- 7.2.1.1 Naïve Bayes 165 -- 7.2.1.2 Decision Trees 167 -- 7.2.2 Ensemble Learning 169 -- 7.2.2.1 Bagging 170 -- 7.2.2.2 Boosting: AdaBoost 171 -- 7.2.3 Deep Learning 172 -- 7.2.3.1 Supervised Learning: Deep Residual Networks 173 -- 7.2.3.2 Effect of Feature Learning-Powered Diagnosis 176 -- 7.3 Techniques for Prognostics 178 -- 7.3.1 Regression Analysis 178 -- 7.3.1.1 Linear Regression 178 -- 7.3.1.2 Polynomial Regression 180 -- 7.3.1.3 Ridge Regression 181 -- 7.3.1.4 LASSO Regression 182 -- 7.3.1.5 Elastic Net Regression 183 -- 7.3.1.6 k-Nearest Neighbors Regression 183 -- 7.3.1.7 Support Vector Regression 184 -- 7.3.2 Particle Filtering 185 -- 7.3.2.1 Fundamentals of Particle Filtering 186 -- 7.3.2.2 Resampling Methods - A Review 187 -- References 189 -- 8 Uncertainty Representation, Quantification, and Management in Prognostics 193 /Shankar Sankararaman -- 8.1 Introduction 193 -- 8.2 Sources of Uncertainty in PHM 196 -- 8.3 Formal Treatment of Uncertainty in PHM 199 -- 8.3.1 Problem 1: Uncertainty Representation and Interpretation 199 -- 8.3.2 Problem 2: Uncertainty Quantification 199 -- 8.3.3 Problem 3: Uncertainty Propagation 200 -- 8.3.4 Problem 4: Uncertainty Management 200 -- 8.4 Uncertainty Representation and Interpretation 200 -- 8.4.1 Physical Probabilities and Testing-Based Prediction 201 -- 8.4.1.1 Physical Probability 201 -- 8.4.1.2 Testing-Based Life Prediction 201. 8.4.1.3 Confidence Intervals 202 -- 8.4.2 Subjective Probabilities and Condition-Based Prognostics 202 -- 8.4.2.1 Subjective Probability 202 -- 8.4.2.2 Subjective Probabilities in Condition-Based Prognostics 203 -- 8.4.3 Why is RUL Prediction Uncertain? 203 -- 8.5 Uncertainty Quantification and Propagation for RUL Prediction 203 -- 8.5.1 Computational Framework for Uncertainty Quantification 204 -- 8.5.1.1 Present State Estimation 204 -- 8.5.1.2 Future State Prediction 205 -- 8.5.1.3 RUL Computation 205 -- 8.5.2 RUL Prediction: An Uncertainty Propagation Problem 206 -- 8.5.3 Uncertainty PropagationMethods 206 -- 8.5.3.1 Sampling-Based Methods 207 -- 8.5.3.2 AnalyticalMethods 209 -- 8.5.3.3 Hybrid Methods 209 -- 8.5.3.4 Summary of Methods 209 -- 8.6 Uncertainty Management 210 -- 8.7 Case Study: Uncertainty Quantification in the Power System of an Unmanned Aerial Vehicle 211 -- 8.7.1 Description of the Model 211 -- 8.7.2 Sources of Uncertainty 212 -- 8.7.3 Results: Constant Amplitude Loading Conditions 213 -- 8.7.4 Results: Variable Amplitude Loading Conditions 214 -- 8.7.5 Discussion 214 -- 8.8 Existing Challenges 215 -- 8.8.1 Timely Predictions 215 -- 8.8.2 Uncertainty Characterization 216 -- 8.8.3 Uncertainty Propagation 216 -- 8.8.4 Capturing Distribution Properties 216 -- 8.8.5 Accuracy 216 -- 8.8.6 Uncertainty Bounds 216 -- 8.8.7 Deterministic Calculations 216 -- 8.9 Summary 217 -- References 217 -- 9 PHM Cost and Return on Investment 221 /Peter Sandborn, ChrisWilkinson, Kiri Lee Sharon, Taoufik Jazouli, and Roozbeh Bakhshi -- 9.1 Return on Investment 221 -- 9.1.1 PHM ROI Analyses 222 -- 9.1.2 Financial Costs 224 -- 9.2 PHM Cost-Modeling Terminology and Definitions 225 -- 9.3 PHM Implementation Costs 226 -- 9.3.1 Nonrecurring Costs 226 -- 9.3.2 Recurring Costs 227 -- 9.3.3 Infrastructure Costs 228 -- 9.3.4 Nonmonetary Considerations and Maintenance Culture 228 -- 9.4 Cost Avoidance 229 -- 9.4.1 Maintenance Planning Cost Avoidance 231 -- 9.4.2 Discrete-Event Simulation Maintenance PlanningModel 232. 9.4.3 Fixed-Schedule Maintenance Interval 233 -- 9.4.4 Data-Driven (Precursor to Failure Monitoring) Methods 233 -- 9.4.5 Model-Based (LRU-Independent)Methods 234 -- 9.4.6 Discrete-Event Simulation Implementation Details 236 -- 9.4.7 Operational Profile 237 -- 9.5 Example PHM Cost Analysis 238 -- 9.5.1 Single-Socket Model Results 239 -- 9.5.2 Multiple-Socket Model Results 241 -- 9.6 Example Business Case Construction: Analysis for ROI 246 -- 9.7 Summary 255 -- References 255 -- 10 Valuation and Optimization of PHM-Enabled Maintenance Decisions 261 /Xin Lei, Amir Reza Kashani-Pour, Peter Sandborn, and Taoufik Jazouli -- 10.1 Valuation and Optimization of PHM-Enabled Maintenance Decisions for an Individual System 262 -- 10.1.1 A PHM-Enabled Predictive Maintenance OptimizationModel for an Individual System 263 -- 10.1.2 Case Study: Optimization of PHM-Enabled Maintenance Decisions for an Individual System (Wind Turbine) 265 -- 10.2 Availability 268 -- 10.2.1 The Business of Availability: Outcome-Based Contracts 269 -- 10.2.2 Incorporating Contract Terms into Maintenance Decisions 270 -- 10.2.3 Case Study: Optimization of PHM-Enabled Maintenance Decisions for Systems (Wind Farm) 270 -- 10.3 Future Directions 272 -- 10.3.1 Design for Availability 272 -- 10.3.2 Prognostics-BasedWarranties 275 -- 10.3.3 Contract Engineering 276 -- References 277 -- 11 Health and Remaining Useful Life Estimation of Electronic Circuits 279 /Arvind Sai Sarathi Vasan and Michael G. Pecht -- 11.1 Introduction 279 -- 11.2 RelatedWork 281 -- 11.2.1 Component-Centric Approach 281 -- 11.2.2 Circuit-Centric Approach 282 -- 11.3 Electronic Circuit Health Estimation Through Kernel Learning 285 -- 11.3.1 Kernel-Based Learning 285 -- 11.3.2 Health Estimation Method 286 -- 11.3.2.1 Likelihood-Based Function for Model Selection 288 -- 11.3.2.2 Optimization Approach for Model Selection 289 -- 11.3.3 Implementation Results 292 -- 11.3.3.1 Bandpass Filter Circuit 293 -- 11.3.3.2 DC-DC Buck Converter System 300. 11.4 RUL Prediction Using Model-Based Filtering 306 -- 11.4.1 Prognostics Problem Formulation 306 -- 11.4.2 Circuit DegradationModeling 307 -- 11.4.3 Model-Based Prognostic Methodology 310 -- 11.4.4 Implementation Results 313 -- 11.4.4.1 Low-Pass Filter Circuit 313 -- 11.4.4.2 Voltage Feedback Circuit 315 -- 11.4.4.3 Source of RUL Prediction Error 320 -- 11.4.4.4 Effect of First-Principles-Based Modeling 320 -- 11.5 Summary 322 -- References 324 -- 12 PHM-Based Qualification of Electronics 329 /Preeti S. Chauhan -- 12.1 Why is Product Qualification Important? 329 -- 12.2 Considerations for Product Qualification 331 -- 12.3 Review of Current Qualification Methodologies 334 -- 12.3.1 Standards-Based Qualification 334 -- 12.3.2 Knowledge-Based or PoF-Based Qualification 337 -- 12.3.3 Prognostics and Health Management-Based Qualification 340 -- 12.3.3.1 Data-Driven Techniques 340 -- 12.3.3.2 Fusion Prognostics 343 -- 12.4 Summary 345 -- References 346 -- 13 PHM of Li-ion Batteries 349 /Saurabh Saxena, Yinjiao Xing, andMichael G. Pecht -- 13.1 Introduction 349 -- 13.2 State of Charge Estimation 351 -- 13.2.1 SOC Estimation Case Study I 352 -- 13.2.1.1 NN Model 353 -- 13.2.1.2 Training and Testing Data 354 -- 13.2.1.3 Determination of the NN Structure 355 -- 13.2.1.4 Training and Testing Results 356 -- 13.2.1.5 Application of Unscented Kalman Filter 357 -- 13.2.2 SOC Estimation Case Study II 357 -- 13.2.2.1 OCV-SOC-T Test 358 -- 13.2.2.2 Battery Modeling and Parameter Identification 359 -- 13.2.2.3 OCV-SOC-T Table for Model Improvement 360 -- 13.2.2.4 Validation of the Proposed Model 362 -- 13.2.2.5 Algorithm Implementation for Online Estimation 362 -- 13.3 State of Health Estimation and Prognostics 365 -- 13.3.1 Case Study for Li-ion Battery Prognostics 366 -- 13.3.1.1 Capacity DegradationModel 366 -- 13.3.1.2 Uncertainties in Battery Prognostics 368 -- 13.3.1.3 Model Updating via Bayesian Monte Carlo 368 -- 13.3.1.4 SOH Prognostics and RUL Estimation 369 -- 13.3.1.5 Prognostic Results 371. 13.4 Summary 371 -- References 372 -- 14 PHM of Light-Emitting Diodes 377 /Moon-Hwan Chang, Jiajie Fan, Cheng Qian, and Bo Sun -- 14.1 Introduction 377 -- 14.2 Review of PHM Methodologies for LEDs 378 -- 14.2.1 Overview of Available Prognostic Methods 378 -- 14.2.2 Data-DrivenMethods 379 -- 14.2.2.1 Statistical Regression 379 -- 14.2.2.2 Static Bayesian Network 381 -- 14.2.2.3 Kalman Filtering 382 -- 14.2.2.4 Particle Filtering 383 -- 14.2.2.5 Artificial Neural Network 384 -- 14.2.3 Physics-Based Methods 385 -- 14.2.4 LED System-Level Prognostics 387 -- 14.3 Simulation-Based Modeling and Failure Analysis for LEDs 388 -- 14.3.1 LED Chip-LevelModeling and Failure Analysis 389 -- 14.3.1.1 Electro-optical Simulation of LED Chip 389 -- 14.3.1.2 LED Chip-Level Failure Analysis 393 -- 14.3.2 LED Package-Level Modeling and Failure Analysis 395 -- 14.3.2.1 Thermal and Optical Simulation for Phosphor-Converted White LED Package 395 -- 14.3.2.2 LED Package-Level Failure Analysis 397 -- 14.3.3 LED System-LevelModeling and Failure Analysis 399 -- 14.4 Return-on-Investment Analysis of Applying Health Monitoring to LED Lighting Systems 401 -- 14.4.1 ROI Methodology 403 -- 14.4.2 ROI Analysis of Applying System Health Monitoring to LED Lighting Systems 406 -- 14.4.2.1 Failure Rates and Distributions for ROI Simulation 407 -- 14.4.2.2 Determination of Prognostics Distance 410 -- 14.4.2.3 IPHM, CPHM, and Cu Evaluation 412 -- 14.4.2.4 ROI Evaluation 417 -- 14.5 Summary 419 -- References 420 -- 15 PHM in Healthcare 431 /Mary Capelli-Schellpfeffer,Myeongsu Kang, andMichael G. Pecht -- 15.1 Healthcare in the United States 431 -- 15.2 Considerations in Healthcare 432 -- 15.2.1 Clinical Consideration in ImplantableMedical Devices 432 -- 15.2.2 Considerations in Care Bots 433 -- 15.3 Benefits of PHM 438 -- 15.3.1 Safety Increase 439 -- 15.3.2 Operational Reliability Improvement 440 -- 15.3.3 Mission Availability Increase 440 -- 15.3.4 System’s Service Life Extension 441 -- 15.3.5 Maintenance Effectiveness Increase 441. 15.4 PHM of ImplantableMedical Devices 442 -- 15.5 PHM of Care Bots 444 -- 15.6 Canary-Based Prognostics of Healthcare Devices 445 -- 15.7 Summary 447 -- References 447 -- 16 PHM of Subsea Cables 451 /David Flynn, Christopher Bailey, Pushpa Rajaguru,Wenshuo Tang, and Chunyan Yin -- 16.1 Subsea Cable Market 451 -- 16.2 Subsea Cables 452 -- 16.3 Cable Failures 454 -- 16.3.1 Internal Failures 455 -- 16.3.2 Early-Stage Failures 455 -- 16.3.3 External Failures 455 -- 16.3.4 Environmental Conditions 455 -- 16.3.5 Third-Party Damage 456 -- 16.4 State-of-the-Art Monitoring 457 -- 16.5 Qualifying and Maintaining Subsea Cables 458 -- 16.5.1 Qualifying Subsea Cables 458 -- 16.5.2 Mechanical Tests 458 -- 16.5.3 Maintaining Subsea Cables 459 -- 16.6 Data-Gathering Techniques 460 -- 16.7 Measuring theWear Behavior of Cable Materials 461 -- 16.8 Predicting Cable Movement 463 -- 16.8.1 Sliding Distance Derivation 463 -- 16.8.2 Scouring Depth Calculations 465 -- 16.9 Predicting Cable Degradation 466 -- 16.9.1 Volume Loss due to Abrasion 466 -- 16.9.2 Volume Loss due to Corrosion 466 -- 16.10 Predicting Remaining Useful Life 468 -- 16.11 Case Study 471 -- 16.12 Future Challenges 471 -- 16.12.1 Data-Driven Approach for Random Failures 471 -- 16.12.2 Model-Driven Approach for Environmental Failures 473 -- 16.12.2.1 Fusion-Based PHM 473 -- 16.12.2.2 Sensing Techniques 474 -- 16.13 Summary 474 -- References 475 -- 17 Connected Vehicle Diagnostics and Prognostics 479 /Yilu Zhang and Xinyu Du -- 17.1 Introduction 479 -- 17.2 Design of an Automatic Field Data Analyzer 481 -- 17.2.1 Data Collection Subsystem 482 -- 17.2.2 Information Abstraction Subsystem 482 -- 17.2.3 Root Cause Analysis Subsystem 482 -- 17.2.3.1 Feature-Ranking Module 482 -- 17.2.3.2 Relevant Feature Set Selection 484 -- 17.2.3.3 Results Interpretation 486 -- 17.3 Case Study: CVDP for Vehicle Batteries 486 -- 17.3.1 Brief Background of Vehicle Batteries 486 -- 17.3.2 Applying AFDA for Vehicle Batteries 488 -- 17.3.3 Experimental Results 489. Contents xvii -- 17.3.3.1 Information Abstraction 490 -- 17.3.3.2 Feature Ranking 490 -- 17.3.3.3 Interpretation of Results 495 -- 17.4 Summary 498 -- References 499 -- 18 The Role of PHM at Commercial Airlines 503 /RhondaWalthall and Ravi Rajamani -- 18.1 Evolution of Aviation Maintenance 503 -- 18.2 Stakeholder Expectations for PHM 506 -- 18.2.1 Passenger Expectations 506 -- 18.2.2 Airline/Operator/Owner Expectations 507 -- 18.2.3 Airframe Manufacturer Expectations 509 -- 18.2.4 Engine Manufacturer Expectations 510 -- 18.2.5 System and Component Supplier Expectations 511 -- 18.2.6 MRO Organization Expectations 512 -- 18.3 PHM Implementation 513 -- 18.3.1 SATAA 513 -- 18.4 PHM Applications 517 -- 18.4.1 Engine Health Management (EHM) 517 -- 18.4.1.1 History of EHM 518 -- 18.4.1.2 EHM Infrastructure 519 -- 18.4.1.3 Technologies Associated with EHM 520 -- 18.4.1.4 The Future 523 -- 18.4.2 Auxiliary Power Unit (APU) Health Management 524 -- 18.4.3 Environmental Control System (ECS) and Air Distribution Health Monitoring 525 -- 18.4.4 Landing System Health Monitoring 526 -- 18.4.5 Liquid Cooling System Health Monitoring 526 -- 18.4.6 Nitrogen Generation System (NGS) Health Monitoring 527 -- 18.4.7 Fuel Consumption Monitoring 527 -- 18.4.8 Flight Control Actuation Health Monitoring 528 -- 18.4.9 Electric Power System Health Monitoring 529 -- 18.4.10 Structural Health Monitoring (SHM) 529 -- 18.4.11 Battery Health Management 531 -- 18.5 Summary 532 -- References 533 -- 19 PHM Software for Electronics 535 /Noel Jordan Jameson,Myeongsu Kang, and Jing Tian -- 19.1 PHM Software: CALCE Simulation Assisted Reliability Assessment 535 -- 19.2 PHM Software: Data-Driven 540 -- 19.2.1 Data Flow 541 -- 19.2.2 Master Options 542 -- 19.2.3 Data Pre-processing 543 -- 19.2.4 Feature Discovery 545 -- 19.2.5 Anomaly Detection 546 -- 19.2.6 Diagnostics/Classification 548 -- 19.2.7 Prognostics/Modeling 552 -- 19.2.8 Challenges in Data-Driven PHM Software Development 554 -- 19.3 Summary 557. 20 eMaintenance 559 /Ramin Karim, Phillip Tretten, and Uday Kumar -- 20.1 From Reactive to Proactive Maintenance 559 -- 20.2 The Onset of eMaintenance 560 -- 20.3 MaintenanceManagement System 561 -- 20.3.1 Life-cycle Management 562 -- 20.3.2 eMaintenance Architecture 564 -- 20.4 Sensor Systems 564 -- 20.4.1 Sensor Technology for PHM 565 -- 20.5 Data Analysis 565 -- 20.6 Predictive Maintenance 566 -- 20.7 Maintenance Analytics 567 -- 20.7.1 Maintenance Descriptive Analytics 568 -- 20.7.2 Maintenance Analytics and eMaintenance 568 -- 20.7.3 Maintenance Analytics and Big Data 568 -- 20.8 Knowledge Discovery 570 -- 20.9 Integrated Knowledge Discovery 571 -- 20.10 User Interface for Decision Support 572 -- 20.11 Applications of eMaintenance 572 -- 20.11.1 eMaintenance in Railways 572 -- 20.11.1.1 Railway Cloud: Swedish Railway Data 573 -- 20.11.1.2 Railway Cloud: Service Architecture 573 -- 20.11.1.3 Railway Cloud: Usage Scenario 574 -- 20.11.2 eMaintenance in Manufacturing 574 -- 20.11.3 MEMS Sensors for Bearing Vibration Measurement 576 -- 20.11.4 Wireless Sensors for Temperature Measurement 576 -- 20.11.5 Monitoring Systems 576 -- 20.11.6 eMaintenance Cloud and Servers 578 -- 20.11.7 Dashboard Managers 580 -- 20.11.8 Alarm Servers 580 -- 20.11.9 Cloud Services 581 -- 20.11.10 Graphic User Interfaces 583 -- 20.12 Internet Technology and Optimizing Technology 585 -- References 586 -- 21 Predictive Maintenance in the IoT Era 589 /Rashmi B. Shetty -- 21.1 Background 589 -- 21.1.1 Challenges of a Maintenance Program 590 -- 21.1.2 Evolution of Maintenance Paradigms 590 -- 21.1.3 Preventive Versus Predictive Maintenance 592 -- 21.1.4 P-F Curve 592 -- 21.1.5 Bathtub Curve 594 -- 21.2 Benefits of a Predictive Maintenance Program 595 -- 21.3 Prognostic Model Selection for Predictive Maintenance 596 -- 21.4 Internet ofThings 598 -- 21.4.1 Industrial IoT 598 -- 21.5 Predictive Maintenance Based on IoT 599 -- 21.6 Predictive Maintenance Usage Cases 600 -- 21.7 Machine Learning Techniques for Data-Driven Predictive Maintenance 600. 21.7.1 Supervised Learning 602 -- 21.7.2 Unsupervised Learning 602 -- 21.7.3 Anomaly Detection 602 -- 21.7.4 Multi-class and Binary Classification Models 603 -- 21.7.5 Regression Models 604 -- 21.7.6 Survival Models 604 -- 21.8 Best Practices 604 -- 21.8.1 Define Business Problem and QuantitativeMetrics 605 -- 21.8.2 Identify Assets and Data Sources 605 -- 21.8.3 Data Acquisition and Transformation 606 -- 21.8.4 Build Models 607 -- 21.8.5 Model Selection 607 -- 21.8.6 Predict Outcomes and Transform into Process Insights 608 -- 21.8.7 Operationalize and Deploy 609 -- 21.8.8 Continuous Monitoring 609 -- 21.9 Challenges in a Successful Predictive Maintenance Program 610 -- 21.9.1 Predictive Maintenance Management Success Key Performance Indicators (KPIs) 610 -- 21.10 Summary 611 -- References 611 -- 22 Analysis of PHM Patents for Electronics 613 /Zhenbao Liu, Zhen Jia, Chi-Man Vong, Shuhui Bu, andMichael G. Pecht -- 22.1 Introduction 613 -- 22.2 Analysis of PHM Patents for Electronics 616 -- 22.2.1 Sources of PHM Patents 616 -- 22.2.2 Analysis of PHM Patents 617 -- 22.3 Trend of Electronics PHM 619 -- 22.3.1 Semiconductor Products and Computers 619 -- 22.3.2 Batteries 622 -- 22.3.3 Electric Motors 626 -- 22.3.4 Circuits and Systems 629 -- 22.3.5 Electrical Devices in Automobiles and Airplanes 631 -- 22.3.6 Networks and Communication Facilities 634 -- 22.3.7 Others 636 -- 22.4 Summary 638 -- References 639 -- 23 A PHM Roadmap for Electronics-Rich Systems 64 /Michael G. Pecht -- 23.1 Introduction 649 -- 23.2 Roadmap Classifications 650 -- 23.2.1 PHM at the Component Level 651 -- 23.2.1.1 PHM for Integrated Circuits 652 -- 23.2.1.2 High-Power Switching Electronics 652 -- 23.2.1.3 Built-In Prognostics for Components and Circuit Boards 653 -- 23.2.1.4 Photo-Electronics Prognostics 654 -- 23.2.1.5 Interconnect andWiring Prognostics 656 -- 23.2.2 PHM at the System Level 657 -- 23.2.2.1 Legacy Systems 657 -- 23.2.2.2 Environmental and OperationalMonitoring 659 -- 23.2.2.3 LRU to Device Level 659. 23.2.2.4 Dynamic Reconfiguration 659 -- 23.2.2.5 System Power Management and PHM 660 -- 23.2.2.6 PHM as Knowledge Infrastructure for System Development 660 -- 23.2.2.7 Prognostics for Software 660 -- 23.2.2.8 PHM for Mitigation of Reliability and Safety Risks 661 -- 23.2.2.9 PHM in Supply Chain Management and Product Maintenance 662 -- 23.3 Methodology Development 663 -- 23.3.1 Best Algorithms 664 -- 23.3.1.1 Approaches to Training 667 -- 23.3.1.2 Active Learning for Unlabeled Data 667 -- 23.3.1.3 Sampling Techniques and Cost-Sensitive Learning for Imbalanced Data 668 -- 23.3.1.4 Transfer Learning for Knowledge Transfer 668 -- 23.3.1.5 Internet ofThings and Big Data Analytics 669 -- 23.3.2 Verification and Validation 670 -- 23.3.3 Long-Term PHM Studies 671 -- 23.3.4 PHM for Storage 671 -- 23.3.5 PHM for No-Fault-Found/Intermittent Failures 672 -- 23.3.6 PHM for Products Subjected to Indeterminate Operating Conditions 673 -- 23.4 Nontechnical Barriers 674 -- 23.4.1 Cost, Return on Investment, and Business Case Development 674 -- 23.4.2 Liability and Litigation 676 -- 23.4.2.1 Code Architecture: Proprietary or Open? 676 -- 23.4.2.2 Long-Term Code Maintenance and Upgrades 676 -- 23.4.2.3 False Alarms, Missed Alarms, and Life-Safety Implications 677 -- 23.4.2.4 Warranty Restructuring 677 -- 23.4.3 Maintenance Culture 677 -- 23.4.4 Contract Structure 677 -- 23.4.5 Role of Standards Organizations 678 -- 23.4.5.1 IEEE Reliability Society and PHM Efforts 678 -- 23.4.5.2 SAE PHM Standards 678 -- 23.4.5.3 PHM Society 679 -- 23.4.6 Licensing and Entitlement Management 680 -- References 680 -- Appendix A Commercially Available Sensor Systems for PHM 691 -- A.1 SmartButton - ACR Systems 691 -- A.2 OWL 400 - ACR Systems 693 -- A.3 SAVERTM 3X90 - Lansmont Instruments 695 -- A.4 G-Link®-LXRS®- LORD MicroStrain®Sensing Systems 697 -- A.5 V-Link®-LXRS®- LORD MicroStrain Sensing Systems 699 -- A.6 3DM-GX4-25TM - LORD MicroStrain Sensing Systems 702 -- A.7 IEPE-LinkTM-LXRS®- LORD MicroStrain Sensing Systems 704. A.8 ICHM®20/20 - Oceana Sensor 706 -- A.9 EnvironmentalMonitoring System 200TM - Upsite Technologies 708 -- A.10 S2NAP®- RLWInc. 710 -- A.11 SR1 Strain Gage Indicator - Advance Instrument Inc. 712 -- A.12 P3 Strain Indicator and Recorder - Micro-Measurements 714 -- A.13 Airscale Suspension-BasedWeighing System - VPG Inc. 716 -- A.14 Radio Microlog - Transmission Dynamics 718 -- Appendix B Journals and Conference Proceedings Related to PHM 721 -- B.1 Journals 721 -- B.2 Conference Proceedings 722 -- Appendix C Glossary of Terms and Definitions 725 -- Index 731. |
| Record Nr. | UNINA-9910466607203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Prognostics and health management of electronics : fundamentals, machine learning, and internet of things / / edited by Michael Pecht and Myeongsu Kang
| Prognostics and health management of electronics : fundamentals, machine learning, and internet of things / / edited by Michael Pecht and Myeongsu Kang |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2018 |
| Descrizione fisica | 1 online resource (808 pages) |
| Disciplina | 621.381028/8 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico | Electronic systems - Maintenance and repair |
| ISBN |
1-119-51535-1
1-119-51532-7 1-119-51530-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
List of Contributors xxiii -- Preface xxvii -- About the Contributors xxxv -- Acknowledgment xlvii -- List of Abbreviations xlix -- 1 Introduction to PHM 1 /Michael G. Pecht andMyeongsu Kang -- 1.1 Reliability and Prognostics 1 -- 1.2 PHM for Electronics 3 -- 1.3 PHM Approaches 6 -- 1.3.1 PoF-Based Approach 6 -- 1.3.1.1 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 7 -- 1.3.1.2 Life-Cycle Load Monitoring 8 -- 1.3.1.3 Data Reduction and Load Feature Extraction 10 -- 1.3.1.4 Data Assessment and Remaining Life Calculation 12 -- 1.3.1.5 Uncertainty Implementation and Assessment 13 -- 1.3.2 Canaries 14 -- 1.3.3 Data-Driven Approach 16 -- 1.3.3.1 Monitoring and Reasoning of Failure Precursors 16 -- 1.3.3.2 Data Analytics and Machine Learning 20 -- 1.3.4 Fusion Approach 23 -- 1.4 Implementation of PHM in a System of Systems 24 -- 1.5 PHM in the Internet ofThings (IoT) Era 26 -- 1.5.1 IoT-Enabled PHM Applications: Manufacturing 27 -- 1.5.2 IoT-Enabled PHM Applications: Energy Generation 27 -- 1.5.3 IoT-Enabled PHM Applications: Transportation and Logistics 28 -- 1.5.4 IoT-Enabled PHM Applications: Automobiles 28 -- 1.5.5 IoT-Enabled PHM Applications: Medical Consumer Products 29 -- 1.5.6 IoT-Enabled PHM Applications:Warranty Services 29 -- 1.5.7 IoT-Enabled PHM Applications: Robotics 30 -- 1.6 Summary 30 -- References 30 -- 2 Sensor Systems for PHM 39 /Hyunseok Oh,Michael H. Azarian, Shunfeng Cheng, andMichael G. Pecht -- 2.1 Sensor and Sensing Principles 39 -- 2.1.1 Thermal Sensors 40 -- 2.1.2 Electrical Sensors 41 -- 2.1.3 Mechanical Sensors 42 -- 2.1.4 Chemical Sensors 42 -- 2.1.5 Humidity Sensors 44 -- 2.1.6 Biosensors 44 -- 2.1.7 Optical Sensors 45 -- 2.1.8 Magnetic Sensors 45 -- 2.2 Sensor Systems for PHM 46 -- 2.2.1 Parameters to be Monitored 47 -- 2.2.2 Sensor System Performance 48 -- 2.2.3 Physical Attributes of Sensor Systems 48 -- 2.2.4 Functional Attributes of Sensor Systems 49 -- 2.2.4.1 Onboard Power and Power Management 49 -- 2.2.4.2 Onboard Memory and Memory Management 50.
2.2.4.3 Programmable SamplingMode and Sampling Rate 51 -- 2.2.4.4 Signal Processing Software 51 -- 2.2.4.5 Fast and Convenient Data Transmission 52 -- 2.2.5 Reliability 53 -- 2.2.6 Availability 53 -- 2.2.7 Cost 54 -- 2.3 Sensor Selection 54 -- 2.4 Examples of Sensor Systems for PHM Implementation 54 -- 2.5 Emerging Trends in Sensor Technology for PHM 59 -- References 60 -- 3 Physics-of-Failure Approach to PHM 61 /Shunfeng Cheng, Nagarajan Raghavan, Jie Gu, Sony Mathew, and Michael G. Pecht -- 3.1 PoF-Based PHM Methodology 61 -- 3.2 Hardware Configuration 62 -- 3.3 Loads 63 -- 3.4 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 64 -- 3.4.1 Examples of FMMEA for Electronic Devices 68 -- 3.5 Stress Analysis 71 -- 3.6 Reliability Assessment and Remaining-Life Predictions 73 -- 3.7 Outputs from PoF-Based PHM 77 -- 3.8 Caution and Concerns in the Use of PoF-Based PHM 78 -- 3.9 Combining PoF with Data-Driven Prognosis 80 -- References 81 -- 4 Machine Learning: Fundamentals 85 /Myeongsu Kang and Noel Jordan Jameson -- 4.1 Types of Machine Learning 85 -- 4.1.1 Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning 86 -- 4.1.2 Batch and Online Learning 88 -- 4.1.3 Instance-Based and Model-Based Learning 89 -- 4.2 Probability Theory in Machine Learning: Fundamentals 90 -- 4.2.1 Probability Space and Random Variables 91 -- 4.2.2 Distributions, Joint Distributions, and Marginal Distributions 91 -- 4.2.3 Conditional Distributions 91 -- 4.2.4 Independence 92 -- 4.2.5 Chain Rule and Bayes Rule 92 -- 4.3 Probability Mass Function and Probability Density Function 93 -- 4.3.1 Probability Mass Function 93 -- 4.3.2 Probability Density Function 93 -- 4.4 Mean, Variance, and Covariance Estimation 94 -- 4.4.1 Mean 94 -- 4.4.2 Variance 94 -- 4.4.3 Robust Covariance Estimation 95 -- 4.5 Probability Distributions 96 -- 4.5.1 Bernoulli Distribution 96 -- 4.5.2 Normal Distribution 96 -- 4.5.3 Uniform Distribution 97 -- 4.6 Maximum Likelihood and Maximum A Posteriori Estimation 97. 4.6.1 Maximum Likelihood Estimation 97 -- 4.6.2 Maximum A Posteriori Estimation 98 -- 4.7 Correlation and Causation 99 -- 4.8 Kernel Trick 100 -- 4.9 Performance Metrics 102 -- 4.9.1 Diagnostic Metrics 102 -- 4.9.2 Prognostic Metrics 105 -- References 107 -- 5 Machine Learning: Data Pre-processing 111 /Myeongsu Kang and Jing Tian -- 5.1 Data Cleaning 111 -- 5.1.1 Missing Data Handling 111 -- 5.1.1.1 Single-Value Imputation Methods 113 -- 5.1.1.2 Model-Based Methods 113 -- 5.2 Feature Scaling 114 -- 5.3 Feature Engineering 116 -- 5.3.1 Feature Extraction 116 -- 5.3.1.1 PCA and Kernel PCA 116 -- 5.3.1.2 LDA and Kernel LDA 118 -- 5.3.1.3 Isomap 119 -- 5.3.1.4 Self-Organizing Map (SOM) 120 -- 5.3.2 Feature Selection 121 -- 5.3.2.1 Feature Selection: FilterMethods 122 -- 5.3.2.2 Feature Selection:WrapperMethods 124 -- 5.3.2.3 Feature Selection: Embedded Methods 124 -- 5.3.2.4 Advanced Feature Selection 125 -- 5.4 Imbalanced Data Handling 125 -- 5.4.1 SamplingMethods for Imbalanced Learning 126 -- 5.4.1.1 Synthetic Minority Oversampling Technique 126 -- 5.4.1.2 Adaptive Synthetic Sampling 126 -- 5.4.1.3 Effect of SamplingMethods for Diagnosis 127 -- References 129 -- 6 Machine Learning: Anomaly Detection 131 /Myeongsu Kang -- 6.1 Introduction 131 -- 6.2 Types of Anomalies 133 -- 6.2.1 Point Anomalies 134 -- 6.2.2 Contextual Anomalies 134 -- 6.2.3 Collective Anomalies 135 -- 6.3 Distance-Based Methods 136 -- 6.3.1 MD Calculation Using an Inverse Matrix Method 137 -- 6.3.2 MD Calculation Using a Gram-Schmidt Orthogonalization Method 137 -- 6.3.3 Decision Rules 138 -- 6.3.3.1 Gamma Distribution:Threshold Selection 138 -- 6.3.3.2 Weibull Distribution:Threshold Selection 139 -- 6.3.3.3 Box-Cox Transformation:Threshold Selection 139 -- 6.4 Clustering-Based Methods 140 -- 6.4.1 k-Means Clustering 141 -- 6.4.2 Fuzzy c-Means Clustering 142 -- 6.4.3 Self-Organizing Maps (SOMs) 142 -- 6.5 Classification-Based Methods 144 -- 6.5.1 One-Class Classification 145 -- 6.5.1.1 One-Class Support Vector Machines 145. 6.5.1.2 k-Nearest Neighbors 148 -- 6.5.2 Multi-Class Classification 149 -- 6.5.2.1 Multi-Class Support Vector Machines 149 -- 6.5.2.2 Neural Networks 151 -- 6.6 StatisticalMethods 153 -- 6.6.1 Sequential Probability Ratio Test 154 -- 6.6.2 Correlation Analysis 156 -- 6.7 Anomaly Detection with No System Health Profile 156 -- 6.8 Challenges in Anomaly Detection 158 -- References 159 -- 7 Machine Learning: Diagnostics and Prognostics 163 /Myeongsu Kang -- 7.1 Overview of Diagnosis and Prognosis 163 -- 7.2 Techniques for Diagnostics 165 -- 7.2.1 Supervised Machine Learning Algorithms 165 -- 7.2.1.1 Naïve Bayes 165 -- 7.2.1.2 Decision Trees 167 -- 7.2.2 Ensemble Learning 169 -- 7.2.2.1 Bagging 170 -- 7.2.2.2 Boosting: AdaBoost 171 -- 7.2.3 Deep Learning 172 -- 7.2.3.1 Supervised Learning: Deep Residual Networks 173 -- 7.2.3.2 Effect of Feature Learning-Powered Diagnosis 176 -- 7.3 Techniques for Prognostics 178 -- 7.3.1 Regression Analysis 178 -- 7.3.1.1 Linear Regression 178 -- 7.3.1.2 Polynomial Regression 180 -- 7.3.1.3 Ridge Regression 181 -- 7.3.1.4 LASSO Regression 182 -- 7.3.1.5 Elastic Net Regression 183 -- 7.3.1.6 k-Nearest Neighbors Regression 183 -- 7.3.1.7 Support Vector Regression 184 -- 7.3.2 Particle Filtering 185 -- 7.3.2.1 Fundamentals of Particle Filtering 186 -- 7.3.2.2 Resampling Methods - A Review 187 -- References 189 -- 8 Uncertainty Representation, Quantification, and Management in Prognostics 193 /Shankar Sankararaman -- 8.1 Introduction 193 -- 8.2 Sources of Uncertainty in PHM 196 -- 8.3 Formal Treatment of Uncertainty in PHM 199 -- 8.3.1 Problem 1: Uncertainty Representation and Interpretation 199 -- 8.3.2 Problem 2: Uncertainty Quantification 199 -- 8.3.3 Problem 3: Uncertainty Propagation 200 -- 8.3.4 Problem 4: Uncertainty Management 200 -- 8.4 Uncertainty Representation and Interpretation 200 -- 8.4.1 Physical Probabilities and Testing-Based Prediction 201 -- 8.4.1.1 Physical Probability 201 -- 8.4.1.2 Testing-Based Life Prediction 201. 8.4.1.3 Confidence Intervals 202 -- 8.4.2 Subjective Probabilities and Condition-Based Prognostics 202 -- 8.4.2.1 Subjective Probability 202 -- 8.4.2.2 Subjective Probabilities in Condition-Based Prognostics 203 -- 8.4.3 Why is RUL Prediction Uncertain? 203 -- 8.5 Uncertainty Quantification and Propagation for RUL Prediction 203 -- 8.5.1 Computational Framework for Uncertainty Quantification 204 -- 8.5.1.1 Present State Estimation 204 -- 8.5.1.2 Future State Prediction 205 -- 8.5.1.3 RUL Computation 205 -- 8.5.2 RUL Prediction: An Uncertainty Propagation Problem 206 -- 8.5.3 Uncertainty PropagationMethods 206 -- 8.5.3.1 Sampling-Based Methods 207 -- 8.5.3.2 AnalyticalMethods 209 -- 8.5.3.3 Hybrid Methods 209 -- 8.5.3.4 Summary of Methods 209 -- 8.6 Uncertainty Management 210 -- 8.7 Case Study: Uncertainty Quantification in the Power System of an Unmanned Aerial Vehicle 211 -- 8.7.1 Description of the Model 211 -- 8.7.2 Sources of Uncertainty 212 -- 8.7.3 Results: Constant Amplitude Loading Conditions 213 -- 8.7.4 Results: Variable Amplitude Loading Conditions 214 -- 8.7.5 Discussion 214 -- 8.8 Existing Challenges 215 -- 8.8.1 Timely Predictions 215 -- 8.8.2 Uncertainty Characterization 216 -- 8.8.3 Uncertainty Propagation 216 -- 8.8.4 Capturing Distribution Properties 216 -- 8.8.5 Accuracy 216 -- 8.8.6 Uncertainty Bounds 216 -- 8.8.7 Deterministic Calculations 216 -- 8.9 Summary 217 -- References 217 -- 9 PHM Cost and Return on Investment 221 /Peter Sandborn, ChrisWilkinson, Kiri Lee Sharon, Taoufik Jazouli, and Roozbeh Bakhshi -- 9.1 Return on Investment 221 -- 9.1.1 PHM ROI Analyses 222 -- 9.1.2 Financial Costs 224 -- 9.2 PHM Cost-Modeling Terminology and Definitions 225 -- 9.3 PHM Implementation Costs 226 -- 9.3.1 Nonrecurring Costs 226 -- 9.3.2 Recurring Costs 227 -- 9.3.3 Infrastructure Costs 228 -- 9.3.4 Nonmonetary Considerations and Maintenance Culture 228 -- 9.4 Cost Avoidance 229 -- 9.4.1 Maintenance Planning Cost Avoidance 231 -- 9.4.2 Discrete-Event Simulation Maintenance PlanningModel 232. 9.4.3 Fixed-Schedule Maintenance Interval 233 -- 9.4.4 Data-Driven (Precursor to Failure Monitoring) Methods 233 -- 9.4.5 Model-Based (LRU-Independent)Methods 234 -- 9.4.6 Discrete-Event Simulation Implementation Details 236 -- 9.4.7 Operational Profile 237 -- 9.5 Example PHM Cost Analysis 238 -- 9.5.1 Single-Socket Model Results 239 -- 9.5.2 Multiple-Socket Model Results 241 -- 9.6 Example Business Case Construction: Analysis for ROI 246 -- 9.7 Summary 255 -- References 255 -- 10 Valuation and Optimization of PHM-Enabled Maintenance Decisions 261 /Xin Lei, Amir Reza Kashani-Pour, Peter Sandborn, and Taoufik Jazouli -- 10.1 Valuation and Optimization of PHM-Enabled Maintenance Decisions for an Individual System 262 -- 10.1.1 A PHM-Enabled Predictive Maintenance OptimizationModel for an Individual System 263 -- 10.1.2 Case Study: Optimization of PHM-Enabled Maintenance Decisions for an Individual System (Wind Turbine) 265 -- 10.2 Availability 268 -- 10.2.1 The Business of Availability: Outcome-Based Contracts 269 -- 10.2.2 Incorporating Contract Terms into Maintenance Decisions 270 -- 10.2.3 Case Study: Optimization of PHM-Enabled Maintenance Decisions for Systems (Wind Farm) 270 -- 10.3 Future Directions 272 -- 10.3.1 Design for Availability 272 -- 10.3.2 Prognostics-BasedWarranties 275 -- 10.3.3 Contract Engineering 276 -- References 277 -- 11 Health and Remaining Useful Life Estimation of Electronic Circuits 279 /Arvind Sai Sarathi Vasan and Michael G. Pecht -- 11.1 Introduction 279 -- 11.2 RelatedWork 281 -- 11.2.1 Component-Centric Approach 281 -- 11.2.2 Circuit-Centric Approach 282 -- 11.3 Electronic Circuit Health Estimation Through Kernel Learning 285 -- 11.3.1 Kernel-Based Learning 285 -- 11.3.2 Health Estimation Method 286 -- 11.3.2.1 Likelihood-Based Function for Model Selection 288 -- 11.3.2.2 Optimization Approach for Model Selection 289 -- 11.3.3 Implementation Results 292 -- 11.3.3.1 Bandpass Filter Circuit 293 -- 11.3.3.2 DC-DC Buck Converter System 300. 11.4 RUL Prediction Using Model-Based Filtering 306 -- 11.4.1 Prognostics Problem Formulation 306 -- 11.4.2 Circuit DegradationModeling 307 -- 11.4.3 Model-Based Prognostic Methodology 310 -- 11.4.4 Implementation Results 313 -- 11.4.4.1 Low-Pass Filter Circuit 313 -- 11.4.4.2 Voltage Feedback Circuit 315 -- 11.4.4.3 Source of RUL Prediction Error 320 -- 11.4.4.4 Effect of First-Principles-Based Modeling 320 -- 11.5 Summary 322 -- References 324 -- 12 PHM-Based Qualification of Electronics 329 /Preeti S. Chauhan -- 12.1 Why is Product Qualification Important? 329 -- 12.2 Considerations for Product Qualification 331 -- 12.3 Review of Current Qualification Methodologies 334 -- 12.3.1 Standards-Based Qualification 334 -- 12.3.2 Knowledge-Based or PoF-Based Qualification 337 -- 12.3.3 Prognostics and Health Management-Based Qualification 340 -- 12.3.3.1 Data-Driven Techniques 340 -- 12.3.3.2 Fusion Prognostics 343 -- 12.4 Summary 345 -- References 346 -- 13 PHM of Li-ion Batteries 349 /Saurabh Saxena, Yinjiao Xing, andMichael G. Pecht -- 13.1 Introduction 349 -- 13.2 State of Charge Estimation 351 -- 13.2.1 SOC Estimation Case Study I 352 -- 13.2.1.1 NN Model 353 -- 13.2.1.2 Training and Testing Data 354 -- 13.2.1.3 Determination of the NN Structure 355 -- 13.2.1.4 Training and Testing Results 356 -- 13.2.1.5 Application of Unscented Kalman Filter 357 -- 13.2.2 SOC Estimation Case Study II 357 -- 13.2.2.1 OCV-SOC-T Test 358 -- 13.2.2.2 Battery Modeling and Parameter Identification 359 -- 13.2.2.3 OCV-SOC-T Table for Model Improvement 360 -- 13.2.2.4 Validation of the Proposed Model 362 -- 13.2.2.5 Algorithm Implementation for Online Estimation 362 -- 13.3 State of Health Estimation and Prognostics 365 -- 13.3.1 Case Study for Li-ion Battery Prognostics 366 -- 13.3.1.1 Capacity DegradationModel 366 -- 13.3.1.2 Uncertainties in Battery Prognostics 368 -- 13.3.1.3 Model Updating via Bayesian Monte Carlo 368 -- 13.3.1.4 SOH Prognostics and RUL Estimation 369 -- 13.3.1.5 Prognostic Results 371. 13.4 Summary 371 -- References 372 -- 14 PHM of Light-Emitting Diodes 377 /Moon-Hwan Chang, Jiajie Fan, Cheng Qian, and Bo Sun -- 14.1 Introduction 377 -- 14.2 Review of PHM Methodologies for LEDs 378 -- 14.2.1 Overview of Available Prognostic Methods 378 -- 14.2.2 Data-DrivenMethods 379 -- 14.2.2.1 Statistical Regression 379 -- 14.2.2.2 Static Bayesian Network 381 -- 14.2.2.3 Kalman Filtering 382 -- 14.2.2.4 Particle Filtering 383 -- 14.2.2.5 Artificial Neural Network 384 -- 14.2.3 Physics-Based Methods 385 -- 14.2.4 LED System-Level Prognostics 387 -- 14.3 Simulation-Based Modeling and Failure Analysis for LEDs 388 -- 14.3.1 LED Chip-LevelModeling and Failure Analysis 389 -- 14.3.1.1 Electro-optical Simulation of LED Chip 389 -- 14.3.1.2 LED Chip-Level Failure Analysis 393 -- 14.3.2 LED Package-Level Modeling and Failure Analysis 395 -- 14.3.2.1 Thermal and Optical Simulation for Phosphor-Converted White LED Package 395 -- 14.3.2.2 LED Package-Level Failure Analysis 397 -- 14.3.3 LED System-LevelModeling and Failure Analysis 399 -- 14.4 Return-on-Investment Analysis of Applying Health Monitoring to LED Lighting Systems 401 -- 14.4.1 ROI Methodology 403 -- 14.4.2 ROI Analysis of Applying System Health Monitoring to LED Lighting Systems 406 -- 14.4.2.1 Failure Rates and Distributions for ROI Simulation 407 -- 14.4.2.2 Determination of Prognostics Distance 410 -- 14.4.2.3 IPHM, CPHM, and Cu Evaluation 412 -- 14.4.2.4 ROI Evaluation 417 -- 14.5 Summary 419 -- References 420 -- 15 PHM in Healthcare 431 /Mary Capelli-Schellpfeffer,Myeongsu Kang, andMichael G. Pecht -- 15.1 Healthcare in the United States 431 -- 15.2 Considerations in Healthcare 432 -- 15.2.1 Clinical Consideration in ImplantableMedical Devices 432 -- 15.2.2 Considerations in Care Bots 433 -- 15.3 Benefits of PHM 438 -- 15.3.1 Safety Increase 439 -- 15.3.2 Operational Reliability Improvement 440 -- 15.3.3 Mission Availability Increase 440 -- 15.3.4 System’s Service Life Extension 441 -- 15.3.5 Maintenance Effectiveness Increase 441. 15.4 PHM of ImplantableMedical Devices 442 -- 15.5 PHM of Care Bots 444 -- 15.6 Canary-Based Prognostics of Healthcare Devices 445 -- 15.7 Summary 447 -- References 447 -- 16 PHM of Subsea Cables 451 /David Flynn, Christopher Bailey, Pushpa Rajaguru,Wenshuo Tang, and Chunyan Yin -- 16.1 Subsea Cable Market 451 -- 16.2 Subsea Cables 452 -- 16.3 Cable Failures 454 -- 16.3.1 Internal Failures 455 -- 16.3.2 Early-Stage Failures 455 -- 16.3.3 External Failures 455 -- 16.3.4 Environmental Conditions 455 -- 16.3.5 Third-Party Damage 456 -- 16.4 State-of-the-Art Monitoring 457 -- 16.5 Qualifying and Maintaining Subsea Cables 458 -- 16.5.1 Qualifying Subsea Cables 458 -- 16.5.2 Mechanical Tests 458 -- 16.5.3 Maintaining Subsea Cables 459 -- 16.6 Data-Gathering Techniques 460 -- 16.7 Measuring theWear Behavior of Cable Materials 461 -- 16.8 Predicting Cable Movement 463 -- 16.8.1 Sliding Distance Derivation 463 -- 16.8.2 Scouring Depth Calculations 465 -- 16.9 Predicting Cable Degradation 466 -- 16.9.1 Volume Loss due to Abrasion 466 -- 16.9.2 Volume Loss due to Corrosion 466 -- 16.10 Predicting Remaining Useful Life 468 -- 16.11 Case Study 471 -- 16.12 Future Challenges 471 -- 16.12.1 Data-Driven Approach for Random Failures 471 -- 16.12.2 Model-Driven Approach for Environmental Failures 473 -- 16.12.2.1 Fusion-Based PHM 473 -- 16.12.2.2 Sensing Techniques 474 -- 16.13 Summary 474 -- References 475 -- 17 Connected Vehicle Diagnostics and Prognostics 479 /Yilu Zhang and Xinyu Du -- 17.1 Introduction 479 -- 17.2 Design of an Automatic Field Data Analyzer 481 -- 17.2.1 Data Collection Subsystem 482 -- 17.2.2 Information Abstraction Subsystem 482 -- 17.2.3 Root Cause Analysis Subsystem 482 -- 17.2.3.1 Feature-Ranking Module 482 -- 17.2.3.2 Relevant Feature Set Selection 484 -- 17.2.3.3 Results Interpretation 486 -- 17.3 Case Study: CVDP for Vehicle Batteries 486 -- 17.3.1 Brief Background of Vehicle Batteries 486 -- 17.3.2 Applying AFDA for Vehicle Batteries 488 -- 17.3.3 Experimental Results 489. Contents xvii -- 17.3.3.1 Information Abstraction 490 -- 17.3.3.2 Feature Ranking 490 -- 17.3.3.3 Interpretation of Results 495 -- 17.4 Summary 498 -- References 499 -- 18 The Role of PHM at Commercial Airlines 503 /RhondaWalthall and Ravi Rajamani -- 18.1 Evolution of Aviation Maintenance 503 -- 18.2 Stakeholder Expectations for PHM 506 -- 18.2.1 Passenger Expectations 506 -- 18.2.2 Airline/Operator/Owner Expectations 507 -- 18.2.3 Airframe Manufacturer Expectations 509 -- 18.2.4 Engine Manufacturer Expectations 510 -- 18.2.5 System and Component Supplier Expectations 511 -- 18.2.6 MRO Organization Expectations 512 -- 18.3 PHM Implementation 513 -- 18.3.1 SATAA 513 -- 18.4 PHM Applications 517 -- 18.4.1 Engine Health Management (EHM) 517 -- 18.4.1.1 History of EHM 518 -- 18.4.1.2 EHM Infrastructure 519 -- 18.4.1.3 Technologies Associated with EHM 520 -- 18.4.1.4 The Future 523 -- 18.4.2 Auxiliary Power Unit (APU) Health Management 524 -- 18.4.3 Environmental Control System (ECS) and Air Distribution Health Monitoring 525 -- 18.4.4 Landing System Health Monitoring 526 -- 18.4.5 Liquid Cooling System Health Monitoring 526 -- 18.4.6 Nitrogen Generation System (NGS) Health Monitoring 527 -- 18.4.7 Fuel Consumption Monitoring 527 -- 18.4.8 Flight Control Actuation Health Monitoring 528 -- 18.4.9 Electric Power System Health Monitoring 529 -- 18.4.10 Structural Health Monitoring (SHM) 529 -- 18.4.11 Battery Health Management 531 -- 18.5 Summary 532 -- References 533 -- 19 PHM Software for Electronics 535 /Noel Jordan Jameson,Myeongsu Kang, and Jing Tian -- 19.1 PHM Software: CALCE Simulation Assisted Reliability Assessment 535 -- 19.2 PHM Software: Data-Driven 540 -- 19.2.1 Data Flow 541 -- 19.2.2 Master Options 542 -- 19.2.3 Data Pre-processing 543 -- 19.2.4 Feature Discovery 545 -- 19.2.5 Anomaly Detection 546 -- 19.2.6 Diagnostics/Classification 548 -- 19.2.7 Prognostics/Modeling 552 -- 19.2.8 Challenges in Data-Driven PHM Software Development 554 -- 19.3 Summary 557. 20 eMaintenance 559 /Ramin Karim, Phillip Tretten, and Uday Kumar -- 20.1 From Reactive to Proactive Maintenance 559 -- 20.2 The Onset of eMaintenance 560 -- 20.3 MaintenanceManagement System 561 -- 20.3.1 Life-cycle Management 562 -- 20.3.2 eMaintenance Architecture 564 -- 20.4 Sensor Systems 564 -- 20.4.1 Sensor Technology for PHM 565 -- 20.5 Data Analysis 565 -- 20.6 Predictive Maintenance 566 -- 20.7 Maintenance Analytics 567 -- 20.7.1 Maintenance Descriptive Analytics 568 -- 20.7.2 Maintenance Analytics and eMaintenance 568 -- 20.7.3 Maintenance Analytics and Big Data 568 -- 20.8 Knowledge Discovery 570 -- 20.9 Integrated Knowledge Discovery 571 -- 20.10 User Interface for Decision Support 572 -- 20.11 Applications of eMaintenance 572 -- 20.11.1 eMaintenance in Railways 572 -- 20.11.1.1 Railway Cloud: Swedish Railway Data 573 -- 20.11.1.2 Railway Cloud: Service Architecture 573 -- 20.11.1.3 Railway Cloud: Usage Scenario 574 -- 20.11.2 eMaintenance in Manufacturing 574 -- 20.11.3 MEMS Sensors for Bearing Vibration Measurement 576 -- 20.11.4 Wireless Sensors for Temperature Measurement 576 -- 20.11.5 Monitoring Systems 576 -- 20.11.6 eMaintenance Cloud and Servers 578 -- 20.11.7 Dashboard Managers 580 -- 20.11.8 Alarm Servers 580 -- 20.11.9 Cloud Services 581 -- 20.11.10 Graphic User Interfaces 583 -- 20.12 Internet Technology and Optimizing Technology 585 -- References 586 -- 21 Predictive Maintenance in the IoT Era 589 /Rashmi B. Shetty -- 21.1 Background 589 -- 21.1.1 Challenges of a Maintenance Program 590 -- 21.1.2 Evolution of Maintenance Paradigms 590 -- 21.1.3 Preventive Versus Predictive Maintenance 592 -- 21.1.4 P-F Curve 592 -- 21.1.5 Bathtub Curve 594 -- 21.2 Benefits of a Predictive Maintenance Program 595 -- 21.3 Prognostic Model Selection for Predictive Maintenance 596 -- 21.4 Internet ofThings 598 -- 21.4.1 Industrial IoT 598 -- 21.5 Predictive Maintenance Based on IoT 599 -- 21.6 Predictive Maintenance Usage Cases 600 -- 21.7 Machine Learning Techniques for Data-Driven Predictive Maintenance 600. 21.7.1 Supervised Learning 602 -- 21.7.2 Unsupervised Learning 602 -- 21.7.3 Anomaly Detection 602 -- 21.7.4 Multi-class and Binary Classification Models 603 -- 21.7.5 Regression Models 604 -- 21.7.6 Survival Models 604 -- 21.8 Best Practices 604 -- 21.8.1 Define Business Problem and QuantitativeMetrics 605 -- 21.8.2 Identify Assets and Data Sources 605 -- 21.8.3 Data Acquisition and Transformation 606 -- 21.8.4 Build Models 607 -- 21.8.5 Model Selection 607 -- 21.8.6 Predict Outcomes and Transform into Process Insights 608 -- 21.8.7 Operationalize and Deploy 609 -- 21.8.8 Continuous Monitoring 609 -- 21.9 Challenges in a Successful Predictive Maintenance Program 610 -- 21.9.1 Predictive Maintenance Management Success Key Performance Indicators (KPIs) 610 -- 21.10 Summary 611 -- References 611 -- 22 Analysis of PHM Patents for Electronics 613 /Zhenbao Liu, Zhen Jia, Chi-Man Vong, Shuhui Bu, andMichael G. Pecht -- 22.1 Introduction 613 -- 22.2 Analysis of PHM Patents for Electronics 616 -- 22.2.1 Sources of PHM Patents 616 -- 22.2.2 Analysis of PHM Patents 617 -- 22.3 Trend of Electronics PHM 619 -- 22.3.1 Semiconductor Products and Computers 619 -- 22.3.2 Batteries 622 -- 22.3.3 Electric Motors 626 -- 22.3.4 Circuits and Systems 629 -- 22.3.5 Electrical Devices in Automobiles and Airplanes 631 -- 22.3.6 Networks and Communication Facilities 634 -- 22.3.7 Others 636 -- 22.4 Summary 638 -- References 639 -- 23 A PHM Roadmap for Electronics-Rich Systems 64 /Michael G. Pecht -- 23.1 Introduction 649 -- 23.2 Roadmap Classifications 650 -- 23.2.1 PHM at the Component Level 651 -- 23.2.1.1 PHM for Integrated Circuits 652 -- 23.2.1.2 High-Power Switching Electronics 652 -- 23.2.1.3 Built-In Prognostics for Components and Circuit Boards 653 -- 23.2.1.4 Photo-Electronics Prognostics 654 -- 23.2.1.5 Interconnect andWiring Prognostics 656 -- 23.2.2 PHM at the System Level 657 -- 23.2.2.1 Legacy Systems 657 -- 23.2.2.2 Environmental and OperationalMonitoring 659 -- 23.2.2.3 LRU to Device Level 659. 23.2.2.4 Dynamic Reconfiguration 659 -- 23.2.2.5 System Power Management and PHM 660 -- 23.2.2.6 PHM as Knowledge Infrastructure for System Development 660 -- 23.2.2.7 Prognostics for Software 660 -- 23.2.2.8 PHM for Mitigation of Reliability and Safety Risks 661 -- 23.2.2.9 PHM in Supply Chain Management and Product Maintenance 662 -- 23.3 Methodology Development 663 -- 23.3.1 Best Algorithms 664 -- 23.3.1.1 Approaches to Training 667 -- 23.3.1.2 Active Learning for Unlabeled Data 667 -- 23.3.1.3 Sampling Techniques and Cost-Sensitive Learning for Imbalanced Data 668 -- 23.3.1.4 Transfer Learning for Knowledge Transfer 668 -- 23.3.1.5 Internet ofThings and Big Data Analytics 669 -- 23.3.2 Verification and Validation 670 -- 23.3.3 Long-Term PHM Studies 671 -- 23.3.4 PHM for Storage 671 -- 23.3.5 PHM for No-Fault-Found/Intermittent Failures 672 -- 23.3.6 PHM for Products Subjected to Indeterminate Operating Conditions 673 -- 23.4 Nontechnical Barriers 674 -- 23.4.1 Cost, Return on Investment, and Business Case Development 674 -- 23.4.2 Liability and Litigation 676 -- 23.4.2.1 Code Architecture: Proprietary or Open? 676 -- 23.4.2.2 Long-Term Code Maintenance and Upgrades 676 -- 23.4.2.3 False Alarms, Missed Alarms, and Life-Safety Implications 677 -- 23.4.2.4 Warranty Restructuring 677 -- 23.4.3 Maintenance Culture 677 -- 23.4.4 Contract Structure 677 -- 23.4.5 Role of Standards Organizations 678 -- 23.4.5.1 IEEE Reliability Society and PHM Efforts 678 -- 23.4.5.2 SAE PHM Standards 678 -- 23.4.5.3 PHM Society 679 -- 23.4.6 Licensing and Entitlement Management 680 -- References 680 -- Appendix A Commercially Available Sensor Systems for PHM 691 -- A.1 SmartButton - ACR Systems 691 -- A.2 OWL 400 - ACR Systems 693 -- A.3 SAVERTM 3X90 - Lansmont Instruments 695 -- A.4 G-Link®-LXRS®- LORD MicroStrain®Sensing Systems 697 -- A.5 V-Link®-LXRS®- LORD MicroStrain Sensing Systems 699 -- A.6 3DM-GX4-25TM - LORD MicroStrain Sensing Systems 702 -- A.7 IEPE-LinkTM-LXRS®- LORD MicroStrain Sensing Systems 704. A.8 ICHM®20/20 - Oceana Sensor 706 -- A.9 EnvironmentalMonitoring System 200TM - Upsite Technologies 708 -- A.10 S2NAP®- RLWInc. 710 -- A.11 SR1 Strain Gage Indicator - Advance Instrument Inc. 712 -- A.12 P3 Strain Indicator and Recorder - Micro-Measurements 714 -- A.13 Airscale Suspension-BasedWeighing System - VPG Inc. 716 -- A.14 Radio Microlog - Transmission Dynamics 718 -- Appendix B Journals and Conference Proceedings Related to PHM 721 -- B.1 Journals 721 -- B.2 Conference Proceedings 722 -- Appendix C Glossary of Terms and Definitions 725 -- Index 731. |
| Record Nr. | UNINA-9910539706803321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Prognostics and health management of electronics : fundamentals, machine learning, and internet of things / / edited by Michael Pecht and Myeongsu Kang
| Prognostics and health management of electronics : fundamentals, machine learning, and internet of things / / edited by Michael Pecht and Myeongsu Kang |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , 2018 |
| Descrizione fisica | 1 online resource (808 pages) |
| Disciplina | 621.381028/8 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico | Electronic systems - Maintenance and repair |
| ISBN |
1-119-51535-1
1-119-51532-7 1-119-51530-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
List of Contributors xxiii -- Preface xxvii -- About the Contributors xxxv -- Acknowledgment xlvii -- List of Abbreviations xlix -- 1 Introduction to PHM 1 /Michael G. Pecht andMyeongsu Kang -- 1.1 Reliability and Prognostics 1 -- 1.2 PHM for Electronics 3 -- 1.3 PHM Approaches 6 -- 1.3.1 PoF-Based Approach 6 -- 1.3.1.1 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 7 -- 1.3.1.2 Life-Cycle Load Monitoring 8 -- 1.3.1.3 Data Reduction and Load Feature Extraction 10 -- 1.3.1.4 Data Assessment and Remaining Life Calculation 12 -- 1.3.1.5 Uncertainty Implementation and Assessment 13 -- 1.3.2 Canaries 14 -- 1.3.3 Data-Driven Approach 16 -- 1.3.3.1 Monitoring and Reasoning of Failure Precursors 16 -- 1.3.3.2 Data Analytics and Machine Learning 20 -- 1.3.4 Fusion Approach 23 -- 1.4 Implementation of PHM in a System of Systems 24 -- 1.5 PHM in the Internet ofThings (IoT) Era 26 -- 1.5.1 IoT-Enabled PHM Applications: Manufacturing 27 -- 1.5.2 IoT-Enabled PHM Applications: Energy Generation 27 -- 1.5.3 IoT-Enabled PHM Applications: Transportation and Logistics 28 -- 1.5.4 IoT-Enabled PHM Applications: Automobiles 28 -- 1.5.5 IoT-Enabled PHM Applications: Medical Consumer Products 29 -- 1.5.6 IoT-Enabled PHM Applications:Warranty Services 29 -- 1.5.7 IoT-Enabled PHM Applications: Robotics 30 -- 1.6 Summary 30 -- References 30 -- 2 Sensor Systems for PHM 39 /Hyunseok Oh,Michael H. Azarian, Shunfeng Cheng, andMichael G. Pecht -- 2.1 Sensor and Sensing Principles 39 -- 2.1.1 Thermal Sensors 40 -- 2.1.2 Electrical Sensors 41 -- 2.1.3 Mechanical Sensors 42 -- 2.1.4 Chemical Sensors 42 -- 2.1.5 Humidity Sensors 44 -- 2.1.6 Biosensors 44 -- 2.1.7 Optical Sensors 45 -- 2.1.8 Magnetic Sensors 45 -- 2.2 Sensor Systems for PHM 46 -- 2.2.1 Parameters to be Monitored 47 -- 2.2.2 Sensor System Performance 48 -- 2.2.3 Physical Attributes of Sensor Systems 48 -- 2.2.4 Functional Attributes of Sensor Systems 49 -- 2.2.4.1 Onboard Power and Power Management 49 -- 2.2.4.2 Onboard Memory and Memory Management 50.
2.2.4.3 Programmable SamplingMode and Sampling Rate 51 -- 2.2.4.4 Signal Processing Software 51 -- 2.2.4.5 Fast and Convenient Data Transmission 52 -- 2.2.5 Reliability 53 -- 2.2.6 Availability 53 -- 2.2.7 Cost 54 -- 2.3 Sensor Selection 54 -- 2.4 Examples of Sensor Systems for PHM Implementation 54 -- 2.5 Emerging Trends in Sensor Technology for PHM 59 -- References 60 -- 3 Physics-of-Failure Approach to PHM 61 /Shunfeng Cheng, Nagarajan Raghavan, Jie Gu, Sony Mathew, and Michael G. Pecht -- 3.1 PoF-Based PHM Methodology 61 -- 3.2 Hardware Configuration 62 -- 3.3 Loads 63 -- 3.4 Failure Modes, Mechanisms, and Effects Analysis (FMMEA) 64 -- 3.4.1 Examples of FMMEA for Electronic Devices 68 -- 3.5 Stress Analysis 71 -- 3.6 Reliability Assessment and Remaining-Life Predictions 73 -- 3.7 Outputs from PoF-Based PHM 77 -- 3.8 Caution and Concerns in the Use of PoF-Based PHM 78 -- 3.9 Combining PoF with Data-Driven Prognosis 80 -- References 81 -- 4 Machine Learning: Fundamentals 85 /Myeongsu Kang and Noel Jordan Jameson -- 4.1 Types of Machine Learning 85 -- 4.1.1 Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning 86 -- 4.1.2 Batch and Online Learning 88 -- 4.1.3 Instance-Based and Model-Based Learning 89 -- 4.2 Probability Theory in Machine Learning: Fundamentals 90 -- 4.2.1 Probability Space and Random Variables 91 -- 4.2.2 Distributions, Joint Distributions, and Marginal Distributions 91 -- 4.2.3 Conditional Distributions 91 -- 4.2.4 Independence 92 -- 4.2.5 Chain Rule and Bayes Rule 92 -- 4.3 Probability Mass Function and Probability Density Function 93 -- 4.3.1 Probability Mass Function 93 -- 4.3.2 Probability Density Function 93 -- 4.4 Mean, Variance, and Covariance Estimation 94 -- 4.4.1 Mean 94 -- 4.4.2 Variance 94 -- 4.4.3 Robust Covariance Estimation 95 -- 4.5 Probability Distributions 96 -- 4.5.1 Bernoulli Distribution 96 -- 4.5.2 Normal Distribution 96 -- 4.5.3 Uniform Distribution 97 -- 4.6 Maximum Likelihood and Maximum A Posteriori Estimation 97. 4.6.1 Maximum Likelihood Estimation 97 -- 4.6.2 Maximum A Posteriori Estimation 98 -- 4.7 Correlation and Causation 99 -- 4.8 Kernel Trick 100 -- 4.9 Performance Metrics 102 -- 4.9.1 Diagnostic Metrics 102 -- 4.9.2 Prognostic Metrics 105 -- References 107 -- 5 Machine Learning: Data Pre-processing 111 /Myeongsu Kang and Jing Tian -- 5.1 Data Cleaning 111 -- 5.1.1 Missing Data Handling 111 -- 5.1.1.1 Single-Value Imputation Methods 113 -- 5.1.1.2 Model-Based Methods 113 -- 5.2 Feature Scaling 114 -- 5.3 Feature Engineering 116 -- 5.3.1 Feature Extraction 116 -- 5.3.1.1 PCA and Kernel PCA 116 -- 5.3.1.2 LDA and Kernel LDA 118 -- 5.3.1.3 Isomap 119 -- 5.3.1.4 Self-Organizing Map (SOM) 120 -- 5.3.2 Feature Selection 121 -- 5.3.2.1 Feature Selection: FilterMethods 122 -- 5.3.2.2 Feature Selection:WrapperMethods 124 -- 5.3.2.3 Feature Selection: Embedded Methods 124 -- 5.3.2.4 Advanced Feature Selection 125 -- 5.4 Imbalanced Data Handling 125 -- 5.4.1 SamplingMethods for Imbalanced Learning 126 -- 5.4.1.1 Synthetic Minority Oversampling Technique 126 -- 5.4.1.2 Adaptive Synthetic Sampling 126 -- 5.4.1.3 Effect of SamplingMethods for Diagnosis 127 -- References 129 -- 6 Machine Learning: Anomaly Detection 131 /Myeongsu Kang -- 6.1 Introduction 131 -- 6.2 Types of Anomalies 133 -- 6.2.1 Point Anomalies 134 -- 6.2.2 Contextual Anomalies 134 -- 6.2.3 Collective Anomalies 135 -- 6.3 Distance-Based Methods 136 -- 6.3.1 MD Calculation Using an Inverse Matrix Method 137 -- 6.3.2 MD Calculation Using a Gram-Schmidt Orthogonalization Method 137 -- 6.3.3 Decision Rules 138 -- 6.3.3.1 Gamma Distribution:Threshold Selection 138 -- 6.3.3.2 Weibull Distribution:Threshold Selection 139 -- 6.3.3.3 Box-Cox Transformation:Threshold Selection 139 -- 6.4 Clustering-Based Methods 140 -- 6.4.1 k-Means Clustering 141 -- 6.4.2 Fuzzy c-Means Clustering 142 -- 6.4.3 Self-Organizing Maps (SOMs) 142 -- 6.5 Classification-Based Methods 144 -- 6.5.1 One-Class Classification 145 -- 6.5.1.1 One-Class Support Vector Machines 145. 6.5.1.2 k-Nearest Neighbors 148 -- 6.5.2 Multi-Class Classification 149 -- 6.5.2.1 Multi-Class Support Vector Machines 149 -- 6.5.2.2 Neural Networks 151 -- 6.6 StatisticalMethods 153 -- 6.6.1 Sequential Probability Ratio Test 154 -- 6.6.2 Correlation Analysis 156 -- 6.7 Anomaly Detection with No System Health Profile 156 -- 6.8 Challenges in Anomaly Detection 158 -- References 159 -- 7 Machine Learning: Diagnostics and Prognostics 163 /Myeongsu Kang -- 7.1 Overview of Diagnosis and Prognosis 163 -- 7.2 Techniques for Diagnostics 165 -- 7.2.1 Supervised Machine Learning Algorithms 165 -- 7.2.1.1 Naïve Bayes 165 -- 7.2.1.2 Decision Trees 167 -- 7.2.2 Ensemble Learning 169 -- 7.2.2.1 Bagging 170 -- 7.2.2.2 Boosting: AdaBoost 171 -- 7.2.3 Deep Learning 172 -- 7.2.3.1 Supervised Learning: Deep Residual Networks 173 -- 7.2.3.2 Effect of Feature Learning-Powered Diagnosis 176 -- 7.3 Techniques for Prognostics 178 -- 7.3.1 Regression Analysis 178 -- 7.3.1.1 Linear Regression 178 -- 7.3.1.2 Polynomial Regression 180 -- 7.3.1.3 Ridge Regression 181 -- 7.3.1.4 LASSO Regression 182 -- 7.3.1.5 Elastic Net Regression 183 -- 7.3.1.6 k-Nearest Neighbors Regression 183 -- 7.3.1.7 Support Vector Regression 184 -- 7.3.2 Particle Filtering 185 -- 7.3.2.1 Fundamentals of Particle Filtering 186 -- 7.3.2.2 Resampling Methods - A Review 187 -- References 189 -- 8 Uncertainty Representation, Quantification, and Management in Prognostics 193 /Shankar Sankararaman -- 8.1 Introduction 193 -- 8.2 Sources of Uncertainty in PHM 196 -- 8.3 Formal Treatment of Uncertainty in PHM 199 -- 8.3.1 Problem 1: Uncertainty Representation and Interpretation 199 -- 8.3.2 Problem 2: Uncertainty Quantification 199 -- 8.3.3 Problem 3: Uncertainty Propagation 200 -- 8.3.4 Problem 4: Uncertainty Management 200 -- 8.4 Uncertainty Representation and Interpretation 200 -- 8.4.1 Physical Probabilities and Testing-Based Prediction 201 -- 8.4.1.1 Physical Probability 201 -- 8.4.1.2 Testing-Based Life Prediction 201. 8.4.1.3 Confidence Intervals 202 -- 8.4.2 Subjective Probabilities and Condition-Based Prognostics 202 -- 8.4.2.1 Subjective Probability 202 -- 8.4.2.2 Subjective Probabilities in Condition-Based Prognostics 203 -- 8.4.3 Why is RUL Prediction Uncertain? 203 -- 8.5 Uncertainty Quantification and Propagation for RUL Prediction 203 -- 8.5.1 Computational Framework for Uncertainty Quantification 204 -- 8.5.1.1 Present State Estimation 204 -- 8.5.1.2 Future State Prediction 205 -- 8.5.1.3 RUL Computation 205 -- 8.5.2 RUL Prediction: An Uncertainty Propagation Problem 206 -- 8.5.3 Uncertainty PropagationMethods 206 -- 8.5.3.1 Sampling-Based Methods 207 -- 8.5.3.2 AnalyticalMethods 209 -- 8.5.3.3 Hybrid Methods 209 -- 8.5.3.4 Summary of Methods 209 -- 8.6 Uncertainty Management 210 -- 8.7 Case Study: Uncertainty Quantification in the Power System of an Unmanned Aerial Vehicle 211 -- 8.7.1 Description of the Model 211 -- 8.7.2 Sources of Uncertainty 212 -- 8.7.3 Results: Constant Amplitude Loading Conditions 213 -- 8.7.4 Results: Variable Amplitude Loading Conditions 214 -- 8.7.5 Discussion 214 -- 8.8 Existing Challenges 215 -- 8.8.1 Timely Predictions 215 -- 8.8.2 Uncertainty Characterization 216 -- 8.8.3 Uncertainty Propagation 216 -- 8.8.4 Capturing Distribution Properties 216 -- 8.8.5 Accuracy 216 -- 8.8.6 Uncertainty Bounds 216 -- 8.8.7 Deterministic Calculations 216 -- 8.9 Summary 217 -- References 217 -- 9 PHM Cost and Return on Investment 221 /Peter Sandborn, ChrisWilkinson, Kiri Lee Sharon, Taoufik Jazouli, and Roozbeh Bakhshi -- 9.1 Return on Investment 221 -- 9.1.1 PHM ROI Analyses 222 -- 9.1.2 Financial Costs 224 -- 9.2 PHM Cost-Modeling Terminology and Definitions 225 -- 9.3 PHM Implementation Costs 226 -- 9.3.1 Nonrecurring Costs 226 -- 9.3.2 Recurring Costs 227 -- 9.3.3 Infrastructure Costs 228 -- 9.3.4 Nonmonetary Considerations and Maintenance Culture 228 -- 9.4 Cost Avoidance 229 -- 9.4.1 Maintenance Planning Cost Avoidance 231 -- 9.4.2 Discrete-Event Simulation Maintenance PlanningModel 232. 9.4.3 Fixed-Schedule Maintenance Interval 233 -- 9.4.4 Data-Driven (Precursor to Failure Monitoring) Methods 233 -- 9.4.5 Model-Based (LRU-Independent)Methods 234 -- 9.4.6 Discrete-Event Simulation Implementation Details 236 -- 9.4.7 Operational Profile 237 -- 9.5 Example PHM Cost Analysis 238 -- 9.5.1 Single-Socket Model Results 239 -- 9.5.2 Multiple-Socket Model Results 241 -- 9.6 Example Business Case Construction: Analysis for ROI 246 -- 9.7 Summary 255 -- References 255 -- 10 Valuation and Optimization of PHM-Enabled Maintenance Decisions 261 /Xin Lei, Amir Reza Kashani-Pour, Peter Sandborn, and Taoufik Jazouli -- 10.1 Valuation and Optimization of PHM-Enabled Maintenance Decisions for an Individual System 262 -- 10.1.1 A PHM-Enabled Predictive Maintenance OptimizationModel for an Individual System 263 -- 10.1.2 Case Study: Optimization of PHM-Enabled Maintenance Decisions for an Individual System (Wind Turbine) 265 -- 10.2 Availability 268 -- 10.2.1 The Business of Availability: Outcome-Based Contracts 269 -- 10.2.2 Incorporating Contract Terms into Maintenance Decisions 270 -- 10.2.3 Case Study: Optimization of PHM-Enabled Maintenance Decisions for Systems (Wind Farm) 270 -- 10.3 Future Directions 272 -- 10.3.1 Design for Availability 272 -- 10.3.2 Prognostics-BasedWarranties 275 -- 10.3.3 Contract Engineering 276 -- References 277 -- 11 Health and Remaining Useful Life Estimation of Electronic Circuits 279 /Arvind Sai Sarathi Vasan and Michael G. Pecht -- 11.1 Introduction 279 -- 11.2 RelatedWork 281 -- 11.2.1 Component-Centric Approach 281 -- 11.2.2 Circuit-Centric Approach 282 -- 11.3 Electronic Circuit Health Estimation Through Kernel Learning 285 -- 11.3.1 Kernel-Based Learning 285 -- 11.3.2 Health Estimation Method 286 -- 11.3.2.1 Likelihood-Based Function for Model Selection 288 -- 11.3.2.2 Optimization Approach for Model Selection 289 -- 11.3.3 Implementation Results 292 -- 11.3.3.1 Bandpass Filter Circuit 293 -- 11.3.3.2 DC-DC Buck Converter System 300. 11.4 RUL Prediction Using Model-Based Filtering 306 -- 11.4.1 Prognostics Problem Formulation 306 -- 11.4.2 Circuit DegradationModeling 307 -- 11.4.3 Model-Based Prognostic Methodology 310 -- 11.4.4 Implementation Results 313 -- 11.4.4.1 Low-Pass Filter Circuit 313 -- 11.4.4.2 Voltage Feedback Circuit 315 -- 11.4.4.3 Source of RUL Prediction Error 320 -- 11.4.4.4 Effect of First-Principles-Based Modeling 320 -- 11.5 Summary 322 -- References 324 -- 12 PHM-Based Qualification of Electronics 329 /Preeti S. Chauhan -- 12.1 Why is Product Qualification Important? 329 -- 12.2 Considerations for Product Qualification 331 -- 12.3 Review of Current Qualification Methodologies 334 -- 12.3.1 Standards-Based Qualification 334 -- 12.3.2 Knowledge-Based or PoF-Based Qualification 337 -- 12.3.3 Prognostics and Health Management-Based Qualification 340 -- 12.3.3.1 Data-Driven Techniques 340 -- 12.3.3.2 Fusion Prognostics 343 -- 12.4 Summary 345 -- References 346 -- 13 PHM of Li-ion Batteries 349 /Saurabh Saxena, Yinjiao Xing, andMichael G. Pecht -- 13.1 Introduction 349 -- 13.2 State of Charge Estimation 351 -- 13.2.1 SOC Estimation Case Study I 352 -- 13.2.1.1 NN Model 353 -- 13.2.1.2 Training and Testing Data 354 -- 13.2.1.3 Determination of the NN Structure 355 -- 13.2.1.4 Training and Testing Results 356 -- 13.2.1.5 Application of Unscented Kalman Filter 357 -- 13.2.2 SOC Estimation Case Study II 357 -- 13.2.2.1 OCV-SOC-T Test 358 -- 13.2.2.2 Battery Modeling and Parameter Identification 359 -- 13.2.2.3 OCV-SOC-T Table for Model Improvement 360 -- 13.2.2.4 Validation of the Proposed Model 362 -- 13.2.2.5 Algorithm Implementation for Online Estimation 362 -- 13.3 State of Health Estimation and Prognostics 365 -- 13.3.1 Case Study for Li-ion Battery Prognostics 366 -- 13.3.1.1 Capacity DegradationModel 366 -- 13.3.1.2 Uncertainties in Battery Prognostics 368 -- 13.3.1.3 Model Updating via Bayesian Monte Carlo 368 -- 13.3.1.4 SOH Prognostics and RUL Estimation 369 -- 13.3.1.5 Prognostic Results 371. 13.4 Summary 371 -- References 372 -- 14 PHM of Light-Emitting Diodes 377 /Moon-Hwan Chang, Jiajie Fan, Cheng Qian, and Bo Sun -- 14.1 Introduction 377 -- 14.2 Review of PHM Methodologies for LEDs 378 -- 14.2.1 Overview of Available Prognostic Methods 378 -- 14.2.2 Data-DrivenMethods 379 -- 14.2.2.1 Statistical Regression 379 -- 14.2.2.2 Static Bayesian Network 381 -- 14.2.2.3 Kalman Filtering 382 -- 14.2.2.4 Particle Filtering 383 -- 14.2.2.5 Artificial Neural Network 384 -- 14.2.3 Physics-Based Methods 385 -- 14.2.4 LED System-Level Prognostics 387 -- 14.3 Simulation-Based Modeling and Failure Analysis for LEDs 388 -- 14.3.1 LED Chip-LevelModeling and Failure Analysis 389 -- 14.3.1.1 Electro-optical Simulation of LED Chip 389 -- 14.3.1.2 LED Chip-Level Failure Analysis 393 -- 14.3.2 LED Package-Level Modeling and Failure Analysis 395 -- 14.3.2.1 Thermal and Optical Simulation for Phosphor-Converted White LED Package 395 -- 14.3.2.2 LED Package-Level Failure Analysis 397 -- 14.3.3 LED System-LevelModeling and Failure Analysis 399 -- 14.4 Return-on-Investment Analysis of Applying Health Monitoring to LED Lighting Systems 401 -- 14.4.1 ROI Methodology 403 -- 14.4.2 ROI Analysis of Applying System Health Monitoring to LED Lighting Systems 406 -- 14.4.2.1 Failure Rates and Distributions for ROI Simulation 407 -- 14.4.2.2 Determination of Prognostics Distance 410 -- 14.4.2.3 IPHM, CPHM, and Cu Evaluation 412 -- 14.4.2.4 ROI Evaluation 417 -- 14.5 Summary 419 -- References 420 -- 15 PHM in Healthcare 431 /Mary Capelli-Schellpfeffer,Myeongsu Kang, andMichael G. Pecht -- 15.1 Healthcare in the United States 431 -- 15.2 Considerations in Healthcare 432 -- 15.2.1 Clinical Consideration in ImplantableMedical Devices 432 -- 15.2.2 Considerations in Care Bots 433 -- 15.3 Benefits of PHM 438 -- 15.3.1 Safety Increase 439 -- 15.3.2 Operational Reliability Improvement 440 -- 15.3.3 Mission Availability Increase 440 -- 15.3.4 System’s Service Life Extension 441 -- 15.3.5 Maintenance Effectiveness Increase 441. 15.4 PHM of ImplantableMedical Devices 442 -- 15.5 PHM of Care Bots 444 -- 15.6 Canary-Based Prognostics of Healthcare Devices 445 -- 15.7 Summary 447 -- References 447 -- 16 PHM of Subsea Cables 451 /David Flynn, Christopher Bailey, Pushpa Rajaguru,Wenshuo Tang, and Chunyan Yin -- 16.1 Subsea Cable Market 451 -- 16.2 Subsea Cables 452 -- 16.3 Cable Failures 454 -- 16.3.1 Internal Failures 455 -- 16.3.2 Early-Stage Failures 455 -- 16.3.3 External Failures 455 -- 16.3.4 Environmental Conditions 455 -- 16.3.5 Third-Party Damage 456 -- 16.4 State-of-the-Art Monitoring 457 -- 16.5 Qualifying and Maintaining Subsea Cables 458 -- 16.5.1 Qualifying Subsea Cables 458 -- 16.5.2 Mechanical Tests 458 -- 16.5.3 Maintaining Subsea Cables 459 -- 16.6 Data-Gathering Techniques 460 -- 16.7 Measuring theWear Behavior of Cable Materials 461 -- 16.8 Predicting Cable Movement 463 -- 16.8.1 Sliding Distance Derivation 463 -- 16.8.2 Scouring Depth Calculations 465 -- 16.9 Predicting Cable Degradation 466 -- 16.9.1 Volume Loss due to Abrasion 466 -- 16.9.2 Volume Loss due to Corrosion 466 -- 16.10 Predicting Remaining Useful Life 468 -- 16.11 Case Study 471 -- 16.12 Future Challenges 471 -- 16.12.1 Data-Driven Approach for Random Failures 471 -- 16.12.2 Model-Driven Approach for Environmental Failures 473 -- 16.12.2.1 Fusion-Based PHM 473 -- 16.12.2.2 Sensing Techniques 474 -- 16.13 Summary 474 -- References 475 -- 17 Connected Vehicle Diagnostics and Prognostics 479 /Yilu Zhang and Xinyu Du -- 17.1 Introduction 479 -- 17.2 Design of an Automatic Field Data Analyzer 481 -- 17.2.1 Data Collection Subsystem 482 -- 17.2.2 Information Abstraction Subsystem 482 -- 17.2.3 Root Cause Analysis Subsystem 482 -- 17.2.3.1 Feature-Ranking Module 482 -- 17.2.3.2 Relevant Feature Set Selection 484 -- 17.2.3.3 Results Interpretation 486 -- 17.3 Case Study: CVDP for Vehicle Batteries 486 -- 17.3.1 Brief Background of Vehicle Batteries 486 -- 17.3.2 Applying AFDA for Vehicle Batteries 488 -- 17.3.3 Experimental Results 489. Contents xvii -- 17.3.3.1 Information Abstraction 490 -- 17.3.3.2 Feature Ranking 490 -- 17.3.3.3 Interpretation of Results 495 -- 17.4 Summary 498 -- References 499 -- 18 The Role of PHM at Commercial Airlines 503 /RhondaWalthall and Ravi Rajamani -- 18.1 Evolution of Aviation Maintenance 503 -- 18.2 Stakeholder Expectations for PHM 506 -- 18.2.1 Passenger Expectations 506 -- 18.2.2 Airline/Operator/Owner Expectations 507 -- 18.2.3 Airframe Manufacturer Expectations 509 -- 18.2.4 Engine Manufacturer Expectations 510 -- 18.2.5 System and Component Supplier Expectations 511 -- 18.2.6 MRO Organization Expectations 512 -- 18.3 PHM Implementation 513 -- 18.3.1 SATAA 513 -- 18.4 PHM Applications 517 -- 18.4.1 Engine Health Management (EHM) 517 -- 18.4.1.1 History of EHM 518 -- 18.4.1.2 EHM Infrastructure 519 -- 18.4.1.3 Technologies Associated with EHM 520 -- 18.4.1.4 The Future 523 -- 18.4.2 Auxiliary Power Unit (APU) Health Management 524 -- 18.4.3 Environmental Control System (ECS) and Air Distribution Health Monitoring 525 -- 18.4.4 Landing System Health Monitoring 526 -- 18.4.5 Liquid Cooling System Health Monitoring 526 -- 18.4.6 Nitrogen Generation System (NGS) Health Monitoring 527 -- 18.4.7 Fuel Consumption Monitoring 527 -- 18.4.8 Flight Control Actuation Health Monitoring 528 -- 18.4.9 Electric Power System Health Monitoring 529 -- 18.4.10 Structural Health Monitoring (SHM) 529 -- 18.4.11 Battery Health Management 531 -- 18.5 Summary 532 -- References 533 -- 19 PHM Software for Electronics 535 /Noel Jordan Jameson,Myeongsu Kang, and Jing Tian -- 19.1 PHM Software: CALCE Simulation Assisted Reliability Assessment 535 -- 19.2 PHM Software: Data-Driven 540 -- 19.2.1 Data Flow 541 -- 19.2.2 Master Options 542 -- 19.2.3 Data Pre-processing 543 -- 19.2.4 Feature Discovery 545 -- 19.2.5 Anomaly Detection 546 -- 19.2.6 Diagnostics/Classification 548 -- 19.2.7 Prognostics/Modeling 552 -- 19.2.8 Challenges in Data-Driven PHM Software Development 554 -- 19.3 Summary 557. 20 eMaintenance 559 /Ramin Karim, Phillip Tretten, and Uday Kumar -- 20.1 From Reactive to Proactive Maintenance 559 -- 20.2 The Onset of eMaintenance 560 -- 20.3 MaintenanceManagement System 561 -- 20.3.1 Life-cycle Management 562 -- 20.3.2 eMaintenance Architecture 564 -- 20.4 Sensor Systems 564 -- 20.4.1 Sensor Technology for PHM 565 -- 20.5 Data Analysis 565 -- 20.6 Predictive Maintenance 566 -- 20.7 Maintenance Analytics 567 -- 20.7.1 Maintenance Descriptive Analytics 568 -- 20.7.2 Maintenance Analytics and eMaintenance 568 -- 20.7.3 Maintenance Analytics and Big Data 568 -- 20.8 Knowledge Discovery 570 -- 20.9 Integrated Knowledge Discovery 571 -- 20.10 User Interface for Decision Support 572 -- 20.11 Applications of eMaintenance 572 -- 20.11.1 eMaintenance in Railways 572 -- 20.11.1.1 Railway Cloud: Swedish Railway Data 573 -- 20.11.1.2 Railway Cloud: Service Architecture 573 -- 20.11.1.3 Railway Cloud: Usage Scenario 574 -- 20.11.2 eMaintenance in Manufacturing 574 -- 20.11.3 MEMS Sensors for Bearing Vibration Measurement 576 -- 20.11.4 Wireless Sensors for Temperature Measurement 576 -- 20.11.5 Monitoring Systems 576 -- 20.11.6 eMaintenance Cloud and Servers 578 -- 20.11.7 Dashboard Managers 580 -- 20.11.8 Alarm Servers 580 -- 20.11.9 Cloud Services 581 -- 20.11.10 Graphic User Interfaces 583 -- 20.12 Internet Technology and Optimizing Technology 585 -- References 586 -- 21 Predictive Maintenance in the IoT Era 589 /Rashmi B. Shetty -- 21.1 Background 589 -- 21.1.1 Challenges of a Maintenance Program 590 -- 21.1.2 Evolution of Maintenance Paradigms 590 -- 21.1.3 Preventive Versus Predictive Maintenance 592 -- 21.1.4 P-F Curve 592 -- 21.1.5 Bathtub Curve 594 -- 21.2 Benefits of a Predictive Maintenance Program 595 -- 21.3 Prognostic Model Selection for Predictive Maintenance 596 -- 21.4 Internet ofThings 598 -- 21.4.1 Industrial IoT 598 -- 21.5 Predictive Maintenance Based on IoT 599 -- 21.6 Predictive Maintenance Usage Cases 600 -- 21.7 Machine Learning Techniques for Data-Driven Predictive Maintenance 600. 21.7.1 Supervised Learning 602 -- 21.7.2 Unsupervised Learning 602 -- 21.7.3 Anomaly Detection 602 -- 21.7.4 Multi-class and Binary Classification Models 603 -- 21.7.5 Regression Models 604 -- 21.7.6 Survival Models 604 -- 21.8 Best Practices 604 -- 21.8.1 Define Business Problem and QuantitativeMetrics 605 -- 21.8.2 Identify Assets and Data Sources 605 -- 21.8.3 Data Acquisition and Transformation 606 -- 21.8.4 Build Models 607 -- 21.8.5 Model Selection 607 -- 21.8.6 Predict Outcomes and Transform into Process Insights 608 -- 21.8.7 Operationalize and Deploy 609 -- 21.8.8 Continuous Monitoring 609 -- 21.9 Challenges in a Successful Predictive Maintenance Program 610 -- 21.9.1 Predictive Maintenance Management Success Key Performance Indicators (KPIs) 610 -- 21.10 Summary 611 -- References 611 -- 22 Analysis of PHM Patents for Electronics 613 /Zhenbao Liu, Zhen Jia, Chi-Man Vong, Shuhui Bu, andMichael G. Pecht -- 22.1 Introduction 613 -- 22.2 Analysis of PHM Patents for Electronics 616 -- 22.2.1 Sources of PHM Patents 616 -- 22.2.2 Analysis of PHM Patents 617 -- 22.3 Trend of Electronics PHM 619 -- 22.3.1 Semiconductor Products and Computers 619 -- 22.3.2 Batteries 622 -- 22.3.3 Electric Motors 626 -- 22.3.4 Circuits and Systems 629 -- 22.3.5 Electrical Devices in Automobiles and Airplanes 631 -- 22.3.6 Networks and Communication Facilities 634 -- 22.3.7 Others 636 -- 22.4 Summary 638 -- References 639 -- 23 A PHM Roadmap for Electronics-Rich Systems 64 /Michael G. Pecht -- 23.1 Introduction 649 -- 23.2 Roadmap Classifications 650 -- 23.2.1 PHM at the Component Level 651 -- 23.2.1.1 PHM for Integrated Circuits 652 -- 23.2.1.2 High-Power Switching Electronics 652 -- 23.2.1.3 Built-In Prognostics for Components and Circuit Boards 653 -- 23.2.1.4 Photo-Electronics Prognostics 654 -- 23.2.1.5 Interconnect andWiring Prognostics 656 -- 23.2.2 PHM at the System Level 657 -- 23.2.2.1 Legacy Systems 657 -- 23.2.2.2 Environmental and OperationalMonitoring 659 -- 23.2.2.3 LRU to Device Level 659. 23.2.2.4 Dynamic Reconfiguration 659 -- 23.2.2.5 System Power Management and PHM 660 -- 23.2.2.6 PHM as Knowledge Infrastructure for System Development 660 -- 23.2.2.7 Prognostics for Software 660 -- 23.2.2.8 PHM for Mitigation of Reliability and Safety Risks 661 -- 23.2.2.9 PHM in Supply Chain Management and Product Maintenance 662 -- 23.3 Methodology Development 663 -- 23.3.1 Best Algorithms 664 -- 23.3.1.1 Approaches to Training 667 -- 23.3.1.2 Active Learning for Unlabeled Data 667 -- 23.3.1.3 Sampling Techniques and Cost-Sensitive Learning for Imbalanced Data 668 -- 23.3.1.4 Transfer Learning for Knowledge Transfer 668 -- 23.3.1.5 Internet ofThings and Big Data Analytics 669 -- 23.3.2 Verification and Validation 670 -- 23.3.3 Long-Term PHM Studies 671 -- 23.3.4 PHM for Storage 671 -- 23.3.5 PHM for No-Fault-Found/Intermittent Failures 672 -- 23.3.6 PHM for Products Subjected to Indeterminate Operating Conditions 673 -- 23.4 Nontechnical Barriers 674 -- 23.4.1 Cost, Return on Investment, and Business Case Development 674 -- 23.4.2 Liability and Litigation 676 -- 23.4.2.1 Code Architecture: Proprietary or Open? 676 -- 23.4.2.2 Long-Term Code Maintenance and Upgrades 676 -- 23.4.2.3 False Alarms, Missed Alarms, and Life-Safety Implications 677 -- 23.4.2.4 Warranty Restructuring 677 -- 23.4.3 Maintenance Culture 677 -- 23.4.4 Contract Structure 677 -- 23.4.5 Role of Standards Organizations 678 -- 23.4.5.1 IEEE Reliability Society and PHM Efforts 678 -- 23.4.5.2 SAE PHM Standards 678 -- 23.4.5.3 PHM Society 679 -- 23.4.6 Licensing and Entitlement Management 680 -- References 680 -- Appendix A Commercially Available Sensor Systems for PHM 691 -- A.1 SmartButton - ACR Systems 691 -- A.2 OWL 400 - ACR Systems 693 -- A.3 SAVERTM 3X90 - Lansmont Instruments 695 -- A.4 G-Link®-LXRS®- LORD MicroStrain®Sensing Systems 697 -- A.5 V-Link®-LXRS®- LORD MicroStrain Sensing Systems 699 -- A.6 3DM-GX4-25TM - LORD MicroStrain Sensing Systems 702 -- A.7 IEPE-LinkTM-LXRS®- LORD MicroStrain Sensing Systems 704. A.8 ICHM®20/20 - Oceana Sensor 706 -- A.9 EnvironmentalMonitoring System 200TM - Upsite Technologies 708 -- A.10 S2NAP®- RLWInc. 710 -- A.11 SR1 Strain Gage Indicator - Advance Instrument Inc. 712 -- A.12 P3 Strain Indicator and Recorder - Micro-Measurements 714 -- A.13 Airscale Suspension-BasedWeighing System - VPG Inc. 716 -- A.14 Radio Microlog - Transmission Dynamics 718 -- Appendix B Journals and Conference Proceedings Related to PHM 721 -- B.1 Journals 721 -- B.2 Conference Proceedings 722 -- Appendix C Glossary of Terms and Definitions 725 -- Index 731. |
| Record Nr. | UNINA-9910810269503321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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