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Advances in fault detection and diagnosis using filtering analysis / / Ziyun Wang, Yan Wang, Zhicheng Ji
| Advances in fault detection and diagnosis using filtering analysis / / Ziyun Wang, Yan Wang, Zhicheng Ji |
| Autore | Wang Ziyun |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (192 pages) |
| Disciplina | 620.0044 |
| Soggetto topico |
Fault location (Engineering)
Kalman filtering |
| ISBN |
981-16-5959-1
981-16-5958-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface I -- Preface II -- Advances in Fault Detection and Diagnosis Using Filtering Analysis -- Contents -- Symbol Description -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Fault Detection and Diagnosis Problem -- 1.2 Classification of Fault Detection and Diagnosis Methods -- 1.2.1 Analytical Model-Based Method -- 1.2.2 Knowledge-Based Method -- 1.2.3 Signal-Processing-Based Method -- 1.3 Fault Classification -- 1.4 An Overview of Fault Diagnosis Process -- 1.4.1 Fault Detection -- 1.4.2 Fault Isolation -- 1.4.3 Fault Identification -- 1.5 Summary of Filtering Methods -- 1.6 Motivation and Objective -- 1.7 Outlines -- References -- 2 Design of State Space Based Fault Diagnosis Filter -- 2.1 Preliminaries and Problem Formulation -- 2.2 Fault Diagnosis Based on Inverse Kalman Filter -- 2.3 Application Study -- 2.4 Concluding Remarks -- References -- 3 Design of Ellipsoid Set-Membership Based Fault Detection Filter -- 3.1 Preliminaries and Problem Formulation -- 3.2 Process of Ellipsoid Set-Membership Method -- 3.3 Finite Data Window Algorithm -- 3.4 Illustrative Simulations -- 3.5 Concluding Remarks -- References -- 4 Design of Polyhedron Set-Membership Based Fault Detection Filter -- 4.1 Preliminaries and Problem Formulation -- 4.2 Polyhedral Cone and the Vertices -- 4.3 Multi-objective Linear Programming -- 4.4 Illustrative Simulations -- 4.5 Application Study -- 4.6 Concluding Remarks -- References -- 5 Design of Interval Set-Membership Based Fault Detection Filter -- 5.1 Preliminaries and Problem Formulation -- 5.2 SIVIA Approach -- 5.2.1 Interval Analysis -- 5.2.2 Set Inversion -- 5.3 Vector Set Inversion Interval Filter -- 5.4 Illustrative Simulations -- 5.5 Application Study -- 5.6 Concluding Remarks -- References -- 6 Design of Orthotopic Set-Membership Based Fault Diagnosis Filter.
6.1 Preliminaries and Problem Formulation -- 6.2 Orthotope and Its Center -- 6.3 Linear Programming -- 6.4 Orthotopic Spatial Extension -- 6.5 Orthotopic-filtering-based Fault Diagnosis Algorithms -- 6.5.1 Fault Detection Criterion -- 6.5.2 Fault Isolation and Identification -- 6.6 Hierarchical Fault Diagnosis -- 6.6.1 Fault Detection -- 6.6.2 Fault Identification -- 6.7 Illustrative Simulations -- 6.8 Application Study -- 6.9 Concluding Remarks -- References -- 7 Fault Diagnosis Method Based on Composite Set-Membership Filter -- 7.1 Preliminaries and Problem Formulation -- 7.2 Directional Expansion Based Fault Diagnosis Algorithm … -- 7.2.1 Fault Detection -- 7.2.2 Fault Isolation -- 7.2.3 Fault Identification -- 7.3 Orthotopic Double Filtering Based State Estimation Algorithm -- 7.3.1 Prediction Step -- 7.3.2 Update Step -- 7.4 Illustrative Simulation -- 7.5 Application Study -- 7.5.1 Application Case 1 -- 7.5.2 Application Case 2 -- 7.5.3 Application Case 3 -- 7.5.4 Application Case 4 -- 7.6 Concluding Remarks -- References -- 8 Summary. |
| Record Nr. | UNINA-9910743383103321 |
Wang Ziyun
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| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Application of Troubleshooting Tools in the Monitored Production Processes / / Petr Baron, Marek Kocisko, and Anton Panda
| Application of Troubleshooting Tools in the Monitored Production Processes / / Petr Baron, Marek Kocisko, and Anton Panda |
| Autore | Baron Petr |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2024] |
| Descrizione fisica | 1 online resource (169 pages) |
| Disciplina | 620.0044 |
| Collana | Management and Industrial Engineering Series |
| Soggetto topico |
Fault location (Engineering)
Reliability (Engineering) |
| ISBN | 3-031-41428-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Vote of Thanks -- Contents -- About the Authors -- Symbols and Abbreviations -- 1 Introduction-Proactive Maintenance Methods -- References -- 2 Technical Diagnostics -- References -- 3 Measurement and Assessment of Technical Systems' Vibrations -- 3.1 Balancing of Rotating Parts of Machines -- 3.1.1 Machine Imbalance -- 3.1.2 Diagnostic Symptoms of Imbalance -- 3.1.3 General Principles for Rotor Balancing -- 3.1.4 Operational Balancing Methods -- 3.1.5 Balancing Rigid Rotors -- 3.1.6 Analysis of the Operating Condition of the Furnace Exhaust Fan Depending on Its Impeller Alignment -- 3.2 Diagnostic of Rolling Bearings -- 3.2.1 Diagnostics of Roller Bearings-Crest Factor -- 3.2.2 Diagnostics of Roller Bearings-HF (High Frequency Emission) -- 3.2.3 Roller Bearing Diagnosis-Kurtosis Factor -- 3.2.4 Diagnostics of Roller Bearings-Envelope Analysis -- 3.2.5 The Correlation of Parameters Measured on Rotary Machine After Reparation of Equipment of the Pulp Production -- 3.2.6 Verification of the Operating Condition of Stationary Industrial Gearbox Through Analysis of Dynamic Signal, Measured on the Pinion Bearing Housing -- 3.2.7 The Dynamic Parameters Correlation Assessment of the Textile Machine High-Speed Bearings in Changed Technological Conditions -- 3.3 Combination the Diagnostic Methods as Suitable Tool for Increasing an Effectivity of Determination the State of Mechanical Nodes -- 3.3.1 The trend's Measurement of Vibrations -- 3.3.2 Tribotechnical Diagnostic -- 3.3.3 The Surface Analysis -- 3.3.4 The Measurement of Roughness -- 3.3.5 The Measurement of Roundness -- 3.3.6 Results of Analyses and Discussion -- 3.4 Application of Methods of Technical Diagnostics by Assessment of Oil Filling Condition in the Process of Running-In of Planetary Gearbox -- 3.4.1 Materials and Methodology.
3.4.2 Results of the Measurements and Experiments -- 3.4.3 Results and Discussion -- 3.5 The Parameter Correlation of Acoustic Emission and High-Frequency Vibrations in the Assessment Process of the Operating State of the Technical System -- 3.5.1 Description of the Measuring-Characteristics of the Machines and Measuring Methods -- References -- 4 Tribotechnical Diagnostics -- 4.1 Classification of Lubricants -- 4.2 Research and Correlation of Diagnostic Methods for Assessment of the State of Oil Filling in Cycloid Gearbox -- 4.2.1 Correlation, Quantification of Measured Parameters, Recommended Limits -- 4.2.2 Discussion of Realized Experiments -- References -- 5 Application of Technical Diagnostics Tools in the Reductors Test Operation -- 5.1 Determination of Methodology and Research of the Influence of the Trial Run of High-Precision Reducers on the Change of Their Characterizing Properties -- 5.1.1 Parameters Characteristic of High-Precision Reducers -- 5.1.2 Description of the Investigated Problem -- 5.1.3 Characteristics of the Diagnostic Methods Applied -- 5.1.4 Conducting Measurements of Characteristic Properties of Bearing Reducers During Their Trial Run -- 5.1.5 Evaluation of Results and Qualitative Assessment of the Impact of the Load During the Trial Run Mode -- 5.1.6 Discussion of the Study Mentioned Above -- 5.2 Design and Implementation of a Diagnostic System for Measuring High-Precision Reducers -- 5.2.1 Design of a Mechatronic Diagnostic System for Measuring High-Precision Reducers -- 5.2.2 Design of Diagnostic Equipment -- 5.2.3 Results and Discussion -- References -- 6 Conclusion -- References -- Index. |
| Record Nr. | UNINA-9910760257903321 |
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Baron Petr
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| Cham, Switzerland : , : Springer, , [2024] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial self-recovery and autonomous health of machine / / Gao Jinji
| Artificial self-recovery and autonomous health of machine / / Gao Jinji |
| Autore | Jinji Gao |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (427 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Fault location (Engineering) Fault tolerance (Engineering) |
| ISBN |
9789811945144
9789811945137 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Introduction-Towards the Era of Self-Recovery -- From "Cure" to "Self-Recovery" -- Development Courses of Automation -- Cybernetics and Engineering Cybernetics -- Engineering Self-Recoveries and Artificial Self-Recovery -- Artificial Self-Recovery Learn from the Principle of "Self-Regulation" in Traditional Chinese Medicine -- Artificial Self-Recovery Expands the Research Field of Bionics and Artificial Intelligence -- Engineering Self-Recoveries and Engineering Cybernetics -- Machine Cyber-Physical-Health Systems -- Accurate Source Tracing Diagnosis and Precise and Stable Self-Recovery Regulation -- Self-Recovery System and the Automatic Control System -- Intrinsic Safety and Autonomous Health -- Contents -- Part I Theoretical Exploration -- 1 Rise of Machines Draw Forth Artificial Self-recovery -- 1.1 Rise of Machines Promotes Changes in the Way of Failure Prevention and Maintenance -- 1.1.1 The Destructive Power of Machine Failure -- 1.1.2 The Rise of Large Industrial Equipment Promotes Failure from Cure to Self-recovery -- 1.2 Medical Science and Machine Failure Self-recovery -- 1.2.1 Similarities and Differences in the Way Humans Struggle with Faults and Diseases -- 1.2.2 Chinese Traditional Medicine and Artificial Self-recovery of Machine Faults -- 1.3 Artificial Self-recovery is the Inevitable Trend of the Intelligent Development of Fault Diagnosis and Treatment -- 1.3.1 Intelligent Development of Fault Diagnosis and Treatment-from Cure to Self-recovery -- 1.3.2 The Necessity of Studying Artificial Self-recovery Theory -- 2 Bionic Artificial Self-recovery and Artificial Intelligence -- 2.1 Artificial Self-recovery Expands the Research Field of Bionics and Artificial Intelligence -- 2.1.1 Artificial Self-recovery Expands the Research Field of Bionics.
2.1.2 Artificial Intelligence Lays the Foundation for Artificial Self-recovery -- 2.1.3 Bionic Artificial Self-recovery Enables Machine to be Autonomous Health -- 2.2 Connotation and Research Direction of Artificial Self-recovery and Artificial Intelligence -- 2.2.1 Conscious Thinking of the Human Brain and Unconscious Thinking of the Human Body -- 2.2.2 Artificial Intelligence Simulates Conscious Thinking to Make Machines Smarter -- 2.2.3 Artificial Self-recovery Simulates the Unconscious Thinking of the Human Body to Make the Machine Autonomous Health -- 2.2.4 Comparison Between Artificial Self-recovery and Artificial Intelligence -- 2.3 AS and AI Developing Courses and the CPS Three-Body Model -- 2.3.1 Developing Courses and Comparison of AS and AI -- 2.3.2 AI-CPS and AS-CPS Three Body Model -- 3 Self-recovery and Automation -- 3.1 The Emergence and Development of Automation and Self-recovery -- 3.1.1 The Emergence and Development of Automation -- 3.1.2 The Emergence and Application Status of Self-recovery Technologies -- 3.1.3 Functional Comparison Between Self-recovery and Automation -- 3.2 Self-recovery Technology Research Field -- 3.2.1 Classification of Fault Self-recovery Technologies -- 3.2.2 Self-repair Technology -- 3.2.3 Compensation Technology -- 3.2.4 Self-protection Technology -- 3.2.5 Fault Self-recovery Regulation Technology -- 4 Engineering Self-recoveries -- 4.1 The Emergence and Development of Cybernetics and Engineering Cybernetics -- 4.1.1 The Emergence and Research Field of Cybernetics -- 4.1.2 The Emergence, Development and Research Fields of Engineering Cybernetics -- 4.2 Industrial Accident Disaster Mechanism and Principles and Laws of Prevention and Control -- 4.2.1 Dissipative Structure Theory and Industrial Accident Disaster Mechanism. 4.2.2 The Formation and Development of Industrial Accidents and the Principles of Prevention and Control -- 4.2.3 Law of Accident Prevention and Control in Industrial Production Complex Systems -- 4.3 Engineering Self-recoveries is a New Field of Cybernetics Research -- 4.3.1 Engineering Self-recoveries Expands the Field of Cybernetics Research -- 4.3.2 Engineering Self-recoveries Enables Entropy Reduction to Keep Machines Orderly and Stable -- 4.3.3 Research Fields of Engineering Self-recoveries -- 4.3.4 Engineering Self-recoveries and Fault Tolerance Theory -- 4.3.5 Engineering Self-recoveries and Cybernetics and Engineering Cybernetics -- 4.4 Assumption of the Self-recoveries Theory -- 4.4.1 Engineering Self-recoveries and Dissipative Structure Theory -- 4.4.2 From Engineering Self-recoveries to Self-recoveries -- 5 Principle of Fault Self-recovery Regulation -- 5.1 Self-recovery Regulation and Intelligent Control -- 5.1.1 Cybernetics and Intelligent Control -- 5.1.2 Overview of Fault Self-recovery Regulation Principle -- 5.1.3 Core Idea of Self-recovery Regulation and Intelligent Control and Comparison of the Both -- 5.1.4 Self-recovery Regulation System and Basic Control System of the Machine -- 5.2 Scientific Basis of Self-recovery Regulation -- 5.2.1 Failure Mechanism and Suppression Law of Mechanical Equipment System -- 5.2.2 Fault Source Tracing Diagnosis and Fault Misdiagnosis Preventive Measures -- 5.2.3 Artificial Intelligence is the Foundation of Fast and Accurate Self-diagnosis of Faults -- 5.3 Self-recovery Regulation System and Targeted Suppression of Vibration Failure -- 5.3.1 Self-recovery Regulation System Based on Parameter/Structure Adaptation -- 5.3.2 Construction of Fault Self-recovery Regulation System -- 5.3.3 Self-recovery Regulation of Large-Scale Process Complex System -- 5.3.4 Vibration Failure Targeted Suppression Method. Part II Technical Research -- 6 Industrial Internet Enables Source Tracing Diagnosis and Intelligent Maintenance -- 6.1 Overview of the Development of Industrial Internet Intelligent Monitoring and Diagnosis -- 6.1.1 Overview of the Development of Industrial Internet Monitoring and Diagnosis at Home and Abroad -- 6.1.2 The Development Course of Networked Intelligent Monitoring and Diagnosis of Petrochemical Equipment in China -- 6.2 Industrial Internet Helps Fast and Accurate Early Warning and Source Tracing Diagnosis -- 6.2.1 Power and Way of the Sustainable Development for the Industrial Internet -- 6.2.2 One Hard-Fast Broadband Synchronous Acquisition and Intelligent Perception of Condition and Operating Data -- 6.2.3 One Soft-Source Tracing Diagnosis Expert System Based on Fault Mechanism and Big Data Analysis -- 6.2.4 One Network-Industrial Internet Remote Monitoring and Condition and Fault Database -- 6.2.5 One Platform-Industrial Cloud and Intelligent Early Warning and Source Tracing Diagnosis Service Platform -- 6.3 Industrial Internet Intelligent Maintenance Engineering Application -- 6.3.1 Industrial Internet Intelligent Maintenance Platform -- 6.3.2 Big Data Analysis of Industrial Internet Intelligent Maintenance -- 6.3.3 Enterprise Group Intelligent Diagnosis and Maintenance Industrial Internet Platform -- 6.3.4 Industrial Internet Source Tracing Diagnosis and Intelligent Maintenance Development Countermeasures -- 7 Autonomous Health and Assistive Rehabilitation and Safety Protection of Dynamic Machinery -- 7.1 Dynamic Machinery Self-recovery and Autonomous Health -- 7.1.1 Overview of Dynamic Machinery Self-recovery and Autonomous Health Technology -- 7.1.2 Autonomous Health Technology of Turbocompressor System -- 7.2 Real-Time Assistive Rehabilitation Engineering for Dynamic Machinery Failure. 7.2.1 Modern Medical Treatment and Equipment for Assistive Rehabilitation Projects -- 7.2.2 Research on Assistive Rehabilitation of Compressor Pipeline Broadband Vibration Tuned Mass Damping [132] -- 7.2.3 TMD Assistive Rehabilitation Project of Compressor Unit Vibration Fault [133] -- 7.3 Dynamic Balance of Turbine Compressor Unit On-Site -- 7.3.1 Field Dynamic Balance of Shaft System Vibration Fault of a Centrifugal Compressor Unit [23] -- 7.3.2 Field Dynamic Balance Without Trial Weight for Unbalanced Vibration of an Ammonia Centrifugal Compressor [135] -- 7.3.3 Research on the Virtual Dynamic Balance of the Multi-rotor Shaft System Without Trial Weight of Turbine Unit -- 7.4 Intelligent Interlocking Safety Protection System for Dynamic Machinery System -- 7.4.1 Necessity and Current Situation of Dynamic Machinery Safety Protection System -- 7.4.2 Intelligent Safety Interlock Protection for Dynamic Machinery -- 7.4.3 Intelligent Safety Interlock Protection for Aeroengine and Industrial Large System -- 8 Turbine Machinery Fault Source Tracing Diagnosis and Targeted Suppression Technology -- 8.1 Targeted Suppression of Multi Frequency Vibration and Instability of Turbine Compressor Rotor -- 8.1.1 Targeted Suppression of Rotor Multi-frequency Vibration by Electromagnetic Forces -- 8.1.2 Targeted Suppression of Centrifugal Compressor Rotor Vibration Based on Electromagnetic Damping Force -- 8.2 Targeted Suppression of Critical Speed and Critical Load Vibration of Turbomachinery -- 8.2.1 Multi-point Targeted Suppression of Critical Speed Vibration of Rotor Shaft System by Magnetorheological Damping [116] -- 8.2.2 Targeted Suppression of Critical Load Vibration of Integral Gear Speed-Increasing Centrifugal Compressor -- 8.3 Self-recovery Regulation of Shaft Displacement and Seal Failure of Turbo-Compressor Unit. 8.3.1 Self-recovery Regulation of Shaft Displacement Fault of Turbo-Compressor Unit. |
| Record Nr. | UNINA-9910627246503321 |
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Jinji Gao
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| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Auxiliary signal design for failure detection / Stephen L. Campbell and Ramine Nikoukhah
| Auxiliary signal design for failure detection / Stephen L. Campbell and Ramine Nikoukhah |
| Autore | Campbell, Stephen La Vern |
| Pubbl/distr/stampa | Princeton, N.J. : Princeton University Press, 2004 |
| Descrizione fisica | vi, 202 p. : ill. ; 24 cm |
| Disciplina | 620.0044 |
| Altri autori (Persone) | Nikoukhah, Ramine, 1961-author |
| Collana | Princeton series in applied mathematics |
| Soggetto topico |
System failures (Engineering)
Fault location (Engineering) Signal processing |
| ISBN | 0691099871 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991000213299707536 |
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Campbell, Stephen La Vern
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| Princeton, N.J. : Princeton University Press, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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Big-data driven intelligent fault diagnosis and prognosis for mechanical systems / / Yaguo Lei, Naipeng Li, Xiang Li
| Big-data driven intelligent fault diagnosis and prognosis for mechanical systems / / Yaguo Lei, Naipeng Li, Xiang Li |
| Autore | Lei Yaguo |
| Pubbl/distr/stampa | Singapore : , : Xi'an Jiaotong University Press : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (292 pages) |
| Disciplina | 005.7 |
| Soggetto topico |
Big data
Fault location (Engineering) Mechanical engineering - Data processing |
| ISBN | 981-16-9131-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Authors -- 1 Introduction and Background -- 1.1 Introduction -- 1.1.1 AI Technologies for Data Processing -- 1.1.2 Big Data-Driven Intelligent Predictive Maintenance -- 1.1.3 Big Data Analytics Platform Practices -- 1.2 Overview of Big Data-Driven PHM -- 1.2.1 Data Acquisition -- 1.2.2 Data Processing -- 1.2.3 Diagnosis -- 1.2.4 Prognosis -- 1.2.5 Maintenance -- 1.3 Preface to Book Chapters -- References -- 2 Conventional Intelligent Fault Diagnosis -- 2.1 Introduction -- 2.2 Typical Neural Network-Based Methods -- 2.2.1 Introduction to Neural Networks -- 2.2.2 Intelligent Diagnosis Using Radial Basis Function Network -- 2.2.3 Intelligent Diagnosis Using Wavelet Neural Network -- 2.2.4 Epilog -- 2.3 Statistical Learning-Based Methods -- 2.3.1 Introduction to Statistical Learning -- 2.3.2 Intelligent Diagnosis Using Support Vector Machine -- 2.3.3 Intelligent Diagnosis Using Relevant Vector Machine -- 2.3.4 Epilog -- 2.4 Conclusions -- References -- 3 Hybrid Intelligent Fault Diagnosis -- 3.1 Introduction -- 3.2 Multiple WKNN Fault Diagnosis -- 3.2.1 Motivation -- 3.2.2 Diagnosis Model Based on Combination of Multiple WKNN -- 3.2.3 Intelligent Diagnosis Case Study of Rolling Element Bearings -- 3.2.4 Epilog -- 3.3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis -- 3.3.1 Motivation -- 3.3.2 Multiple ANFIS Combination with GA -- 3.3.3 Fault Diagnosis Method Based on Multiple ANFIS Combination -- 3.3.4 Intelligent Diagnosis Case of Rolling Element Bearings -- 3.3.5 Epilog -- 3.4 A Multidimensional Hybrid Intelligent Method -- 3.4.1 Motivation -- 3.4.2 Multiple Classifier Combination -- 3.4.3 Diagnosis Method Based on Multiple Classifier Combination -- 3.4.4 Intelligent Diagnosis Case of Gearboxes -- 3.4.5 Epilog -- 3.5 Conclusions -- References -- 4 Deep Transfer Learning-Based Intelligent Fault Diagnosis.
4.1 Introduction -- 4.2 Deep Belief Network for Few-Shot Fault Diagnosis -- 4.2.1 Motivation -- 4.2.2 Deep Belief Network-Based Diagnosis Model with Continual Learning -- 4.2.3 Few-Shot Fault Diagnosis Case of Industrial Robots -- 4.2.4 Epilog -- 4.3 Multi-Layer Adaptation Network for Fault Diagnosis with Unlabeled Data -- 4.3.1 Motivation -- 4.3.2 Multi-Layer Adaptation Network-Based Diagnosis Model -- 4.3.3 Fault Diagnosis Case of Locomotive Bearings with Unlabeled Data -- 4.3.4 Epilog -- 4.4 Deep Partial Adaptation Network for Domain-Asymmetric Fault Diagnosis -- 4.4.1 Motivation -- 4.4.2 Deep Partial Transfer Learning Net-Based Diagnosis Model -- 4.4.3 Partial Transfer Diagnosis of Gearboxes with Domain Asymmetry -- 4.4.4 Epilog -- 4.5 Instance-Level Weighted Adversarial Learning for Open-Set Fault Diagnosis -- 4.5.1 Motivation -- 4.5.2 Instance-Level Weighted Adversarial Learning-Based Diagnosis Model -- 4.5.3 Fault Diagnosis Case of Rolling Bearing Datasets -- 4.5.4 Epilog -- 4.6 Conclusions -- References -- 5 Data-Driven RUL Prediction -- 5.1 Introduction -- 5.2 Deep Separable Convolutional Neural Network-Based RUL Prediction -- 5.2.1 Motivation -- 5.2.2 Deep Separable Convolutional Network -- 5.2.3 Architecture of DSCN -- 5.2.4 RUL Prediction Case of Accelerated Degradation Experiments of Rolling Element Bearings -- 5.2.5 Epilog -- 5.3 Recurrent Convolutional Neural Network-Based RUL Prediction -- 5.3.1 Motivation -- 5.3.2 Recurrent Convolutional Neural Network -- 5.3.3 Architecture of RCNN -- 5.3.4 RUL Prediction Case Study of FEMTO-ST Accelerated Degradation Tests of Rolling Element Bearings -- 5.3.5 Epilog -- 5.4 Multi-scale Convolutional Attention Network-Based RUL Prediction -- 5.4.1 Motivation -- 5.4.2 Multi-scale Convolutional Attention Network -- 5.4.3 Architecture of MSCAN. 5.4.4 RUL Prediction Case of a Life Testing of Milling Cutters -- 5.4.5 Epilog -- 5.5 Conclusions -- References -- 6 Data-Model Fusion RUL Prediction -- 6.1 Introduction -- 6.2 RUL Prediction with Random Fluctuation Variability -- 6.2.1 Motivation -- 6.2.2 RUL Prediction Considering Random Fluctuation Variability -- 6.2.3 RUL Prediction Case of FEMTO-ST Accelerated Degradation Tests of Rolling Element Bearings -- 6.2.4 Epilog -- 6.3 RUL Prediction with Unit-to-Unit Variability -- 6.3.1 Motivation -- 6.3.2 RUL Prediction Model Considering Unit-to-Unit Variability -- 6.3.3 RUL Prediction Case of Turbofan Engine Degradation Dataset -- 6.3.4 Epilog -- 6.4 RUL Prediction with Time-Varying Operational Conditions -- 6.4.1 Motivation -- 6.4.2 RUL Prediction Model Considering Time-Varying Operational Conditions -- 6.4.3 RUL Prediction Case of Accelerated Degradation Experiments of Thrusting Bearings -- 6.4.4 Epilog -- 6.5 RUL Prediction with Dependent Competing Failure Processes -- 6.5.1 Motivation -- 6.5.2 RUL Prediction Model Considering Dependent Competing Failure Processes -- 6.5.3 RUL Prediction Case of Accelerated Degradation Experiments of Rolling Element Bearings -- 6.5.4 Epilog -- 6.6 Conclusions -- References -- Glossary. |
| Record Nr. | UNINA-9910627272303321 |
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Lei Yaguo
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| Singapore : , : Xi'an Jiaotong University Press : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Conference on Control and Fault-Tolerant Systems : [proceedings]
| Conference on Control and Fault-Tolerant Systems : [proceedings] |
| Pubbl/distr/stampa | Piscataway, NJ, : Institute of Electrical and Electronic Engineers |
| Disciplina | 629.8 |
| Soggetto topico |
Automatic control
Fault tolerance (Engineering) Fault location (Engineering) Fault-tolerant computing |
| Soggetto genere / forma | Conference papers and proceedings. |
| ISSN | 2162-1209 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | SysTol |
| Record Nr. | UNISA-996280249203316 |
| Piscataway, NJ, : Institute of Electrical and Electronic Engineers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Conference on Control and Fault-Tolerant Systems : [proceedings]
| Conference on Control and Fault-Tolerant Systems : [proceedings] |
| Pubbl/distr/stampa | Piscataway, NJ, : Institute of Electrical and Electronic Engineers |
| Disciplina | 629.8 |
| Soggetto topico |
Automatic control
Fault tolerance (Engineering) Fault location (Engineering) Fault-tolerant computing |
| Soggetto genere / forma | Conference papers and proceedings. |
| ISSN | 2162-1209 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | SysTol |
| Record Nr. | UNINA-9910626006003321 |
| Piscataway, NJ, : Institute of Electrical and Electronic Engineers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data-driven detection and diagnosis of faults in traction systems of high-speed trains / / Hongtian Chen, Bin Jiang, Ningyun Lu, Wen Chen
| Data-driven detection and diagnosis of faults in traction systems of high-speed trains / / Hongtian Chen, Bin Jiang, Ningyun Lu, Wen Chen |
| Autore | Chen Hongtian |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (xiii, 160 pages) : illustrations |
| Disciplina | 385.22 |
| Collana | Lecture Notes in Intelligent Transportation and Infrastructure |
| Soggetto topico |
Fault location (Engineering)
High speed trains |
| ISBN | 3-030-46263-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Traction Systems and Experimental Platforms -- Basics of Data-driven FDD Methods -- Multi-mode PCA-based FDD Methods -- Probability-relevant PCA-based FDD Methods -- Deep PCA-based FDD Methods -- PCA and Kull back-Leibler Divergence-based FDD Methods -- PCA and Hellinger Distance-based FDD Methods -- Conclusions and Further Work. |
| Record Nr. | UNINA-9910392740503321 |
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Chen Hongtian
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Deep learning-based detection of catenary support component defect and fault in high-speed railways / / Zhigang Liu, Wenqiang Liu, and Junping Zhong
| Deep learning-based detection of catenary support component defect and fault in high-speed railways / / Zhigang Liu, Wenqiang Liu, and Junping Zhong |
| Autore | Liu Zhigang |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] |
| Descrizione fisica | 1 online resource (XIII, 239 p. 212 illus., 149 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Advances in High-speed Rail Technology |
| Soggetto topico |
Deep learning (Machine learning)
Fault location (Engineering) High speed trains |
| ISBN | 981-9909-53-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of Catenary Detection of Electrified Railways -- Advance of Deep Learning -- Catenary Support Components and their Characteristics in High-speed Railways -- Preprocessing of Catenary Support Components’ Images -- Positioning of Catenary Support Components -- Detection of Catenary Support Component Defect and Fault -- Detection of The parameters of Catenary Support Devices based on 3D Point Clouds. |
| Record Nr. | UNINA-9910686480303321 |
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Liu Zhigang
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| Singapore : , : Springer Nature Singapore Pte Ltd., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Diagnostyka
| Diagnostyka |
| Pubbl/distr/stampa | Warszawa, Poland : , : Polskie Towarzystwo Diagnostyki Technicznej |
| Descrizione fisica | 1 online resource |
| Soggetto topico |
Fault location (Engineering)
Engineering systems - Testing Machinery - Monitoring |
| Soggetto genere / forma | Periodicals. |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | pol |
| Record Nr. | UNISA-996462253203316 |
| Warszawa, Poland : , : Polskie Towarzystwo Diagnostyki Technicznej | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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