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Advances in condition monitoring of machinery in non-stationary operations : proceedings of the third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013 / / Giorgio Dalpiaz [and seven others], editors
| Advances in condition monitoring of machinery in non-stationary operations : proceedings of the third International Conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO 2013 / / Giorgio Dalpiaz [and seven others], editors |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Heidelberg [Germany] : , : Springer, , 2014 |
| Descrizione fisica | 1 online resource (xiii, 709 pages) : illustrations (some color) |
| Disciplina |
620
621.8/16 |
| Collana | Lecture Notes in Mechanical Engineering |
| Soggetto topico |
Machinery - Monitoring
Mechanical engineering |
| ISBN | 3-642-39348-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Rolling bearing diagnostics -- Gearbox diagnostics -- Signal processing for machine condition monitoring -- Experimental and numerical modeling of machine dynamics -- Mechanical systems diagnostics. |
| Record Nr. | UNINA-9910299709503321 |
| Heidelberg [Germany] : , : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual
| Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual |
| Autore | Pascual Diego Galar |
| Pubbl/distr/stampa | Boca Raton, Florida : , : CRC Press, , [2015] |
| Descrizione fisica | 1 online resource (528 p.) |
| Disciplina | 658.2020285 |
| Soggetto topico |
Industrial equipment - Maintenance and repair - Data processing
Machinery - Monitoring Artificial intelligence - Industrial applications |
| ISBN |
1-4987-6019-8
0-429-10232-1 1-4665-8406-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Contents; Preface; Acknowledgments; Author; Chapter 1: Massive Field Data Collection: Issues and Challenges; Chapter 2: Condition Monitoring: Available Techniques; Chapter 3: Challenges of Condition Monitoring Using AI Techniques; Chapter 4: Input and Output Data; Chapter 5: Two-Stage Response Surface Approaches to Modeling Drug Interaction; Chapter 6: Nearest Neighbor-Based Techniques; Chapter 7: Cluster-Based Techniques; Chapter 8: Statistical Techniques; Chapter 9: Information Theory-Based Techniques; Chapter 10: Uncertainty Management; Back Cover |
| Record Nr. | UNINA-9910787963003321 |
Pascual Diego Galar
|
||
| Boca Raton, Florida : , : CRC Press, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual
| Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual |
| Autore | Pascual Diego Galar |
| Pubbl/distr/stampa | Boca Raton, Florida : , : CRC Press, , [2015] |
| Descrizione fisica | 1 online resource (528 p.) |
| Disciplina | 658.2020285 |
| Soggetto topico |
Industrial equipment - Maintenance and repair - Data processing
Machinery - Monitoring Artificial intelligence - Industrial applications |
| ISBN |
1-4987-6019-8
0-429-10232-1 1-4665-8406-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Contents; Preface; Acknowledgments; Author; Chapter 1: Massive Field Data Collection: Issues and Challenges; Chapter 2: Condition Monitoring: Available Techniques; Chapter 3: Challenges of Condition Monitoring Using AI Techniques; Chapter 4: Input and Output Data; Chapter 5: Two-Stage Response Surface Approaches to Modeling Drug Interaction; Chapter 6: Nearest Neighbor-Based Techniques; Chapter 7: Cluster-Based Techniques; Chapter 8: Statistical Techniques; Chapter 9: Information Theory-Based Techniques; Chapter 10: Uncertainty Management; Back Cover |
| Record Nr. | UNINA-9910800185003321 |
|
Pascual Diego Galar
|
||
| Boca Raton, Florida : , : CRC Press, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual
| Artificial intelligence tools : decision support systems in condition monitoring and diagnosis / / Diego Galar Pascual |
| Autore | Pascual Diego Galar |
| Pubbl/distr/stampa | Boca Raton, Florida : , : CRC Press, , [2015] |
| Descrizione fisica | 1 online resource (528 p.) |
| Disciplina | 658.2020285 |
| Soggetto topico |
Industrial equipment - Maintenance and repair - Data processing
Machinery - Monitoring Artificial intelligence - Industrial applications |
| ISBN |
1-4987-6019-8
0-429-10232-1 1-4665-8406-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Contents; Preface; Acknowledgments; Author; Chapter 1: Massive Field Data Collection: Issues and Challenges; Chapter 2: Condition Monitoring: Available Techniques; Chapter 3: Challenges of Condition Monitoring Using AI Techniques; Chapter 4: Input and Output Data; Chapter 5: Two-Stage Response Surface Approaches to Modeling Drug Interaction; Chapter 6: Nearest Neighbor-Based Techniques; Chapter 7: Cluster-Based Techniques; Chapter 8: Statistical Techniques; Chapter 9: Information Theory-Based Techniques; Chapter 10: Uncertainty Management; Back Cover |
| Record Nr. | UNINA-9910829136803321 |
|
Pascual Diego Galar
|
||
| Boca Raton, Florida : , : CRC Press, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi
| Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi |
| Autore | Ahmed Hosameldin <1976-> |
| Pubbl/distr/stampa | Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020 |
| Descrizione fisica | 1 online resource (437 pages) |
| Disciplina | 621.80287 |
| Soggetto topico | Machinery - Monitoring |
| ISBN |
1-119-54464-5
1-119-54467-X 1-119-54463-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xvii -- About the Authors xxi -- List of Abbreviations xxiii -- Part I Introduction 1 -- 1 Introduction to Machine Condition Monitoring 3 -- 1.1 Background 3 -- 1.2 Maintenance Approaches for Rotating Machines Failures 4 -- 1.2.1 Corrective Maintenance 4 -- 1.2.2 Preventive Maintenance 5 -- 1.2.2.1 Time-Based Maintenance (TBM) 5 -- 1.2.2.2 Condition-Based Maintenance (CBM) 5 -- 1.3 Applications of MCM 5 -- 1.3.1 Wind Turbines 5 -- 1.3.2 Oil and Gas 6 -- 1.3.3 Aerospace and Defence Industry 6 -- 1.3.4 Automotive 7 -- 1.3.5 Marine Engines 7 -- 1.3.6 Locomotives 7 -- 1.4 Condition Monitoring Techniques 7 -- 1.4.1 Vibration Monitoring 7 -- 1.4.2 Acoustic Emission 8 -- 1.4.3 Fusion of Vibration and Acoustic 8 -- 1.4.4 Motor Current Monitoring 8 -- 1.4.5 Oil Analysis and Lubrication Monitoring 8 -- 1.4.6 Thermography 9 -- 1.4.7 Visual Inspection 9 -- 1.4.8 Performance Monitoring 9 -- 1.4.9 Trend Monitoring 10 -- 1.5 Topic Overview and Scope of the Book 10 -- 1.6 Summary 11 -- References 11 -- 2 Principles of Rotating Machine Vibration Signals 17 -- 2.1 Introduction 17 -- 2.2 Machine Vibration Principles 17 -- 2.3 Sources of Rotating Machines Vibration Signals 20 -- 2.3.1 Rotor Mass Unbalance 21 -- 2.3.2 Misalignment 21 -- 2.3.3 Cracked Shafts 21 -- 2.3.4 Rolling Element Bearings 23 -- 2.3.5 Gears 25 -- 2.4 Types of Vibration Signals 25 -- 2.4.1 Stationary 26 -- 2.4.2 Nonstationary 26 -- 2.5 Vibration Signal Acquisition 26 -- 2.5.1 Displacement Transducers 26 -- 2.5.2 Velocity Transducers 26 -- 2.5.3 Accelerometers 27 -- 2.6 Advantages and Limitations of Vibration Signal Monitoring 27 -- 2.7 Summary 28 -- References 28 -- Part II Vibration Signal Analysis Techniques 31 -- 3 Time Domain Analysis 33 -- 3.1 Introduction 33 -- 3.1.1 Visual Inspection 33 -- 3.1.2 Features-Based Inspection 35 -- 3.2 Statistical Functions 35 -- 3.2.1 Peak Amplitude 36 -- 3.2.2 Mean Amplitude 36 -- 3.2.3 Root Mean Square Amplitude 36 -- 3.2.4 Peak-to-Peak Amplitude 36 -- 3.2.5 Crest Factor (CF) 36.
3.2.6 Variance and Standard Deviation 37 -- 3.2.7 Standard Error 37 -- 3.2.8 Zero Crossing 38 -- 3.2.9 Wavelength 39 -- 3.2.10 Willison Amplitude 39 -- 3.2.11 Slope Sign Change 39 -- 3.2.12 Impulse Factor 39 -- 3.2.13 Margin Factor 40 -- 3.2.14 Shape Factor 40 -- 3.2.15 Clearance Factor 40 -- 3.2.16 Skewness 40 -- 3.2.17 Kurtosis 40 -- 3.2.18 Higher-Order Cumulants (HOCs) 41 -- 3.2.19 Histograms 42 -- 3.2.20 Normal/Weibull Negative Log-Likelihood Value 42 -- 3.2.21 Entropy 42 -- 3.3 Time Synchronous Averaging 44 -- 3.3.1 TSA Signals 44 -- 3.3.2 Residual Signal (RES) 44 -- 3.3.2.1 NA4 44 -- 3.3.2.2 NA4* 45 -- 3.3.3 Difference Signal (DIFS) 45 -- 3.3.3.1 FM4 46 -- 3.3.3.2 M6A 46 -- 3.3.3.3 M8A 46 -- 3.4 Time Series Regressive Models 46 -- 3.4.1 AR Model 47 -- 3.4.2 MA Model 48 -- 3.4.3 ARMA Model 48 -- 3.4.4 ARIMA Model 48 -- 3.5 Filter-Based Methods 49 -- 3.5.1 Demodulation 49 -- 3.5.2 Prony Model 52 -- 3.5.3 Adaptive Noise Cancellation (ANC) 53 -- 3.6 Stochastic Parameter Techniques 54 -- 3.7 Blind Source Separation (BSS) 54 -- 3.8 Summary 55 -- References 56 -- 4 Frequency Domain Analysis 63 -- 4.1 Introduction 63 -- 4.2 Fourier Analysis 64 -- 4.2.1 Fourier Series 64 -- 4.2.2 Discrete Fourier Transform 66 -- 4.2.3 Fast Fourier Transform (FFT) 67 -- 4.3 Envelope Analysis 71 -- 4.4 Frequency Spectrum Statistical Features 73 -- 4.4.1 Arithmetic Mean 73 -- 4.4.2 Geometric Mean 73 -- 4.4.3 Matched Filter RMS 73 -- 4.4.4 The RMS of Spectral Difference 74 -- 4.4.5 The Sum of Squares Spectral Difference 74 -- 4.4.6 High-Order Spectra Techniques 74 -- 4.5 Summary 75 -- References 76 -- 5 Time-Frequency Domain Analysis 79 -- 5.1 Introduction 79 -- 5.2 Short-Time Fourier Transform (STFT) 79 -- 5.3 Wavelet Analysis 82 -- 5.3.1 Wavelet Transform (WT) 82 -- 5.3.1.1 Continuous Wavelet Transform (CWT) 83 -- 5.3.1.2 Discrete Wavelet Transform (DWT) 85 -- 5.3.2 Wavelet Packet Transform (WPT) 89 -- 5.4 Empirical Mode Decomposition (EMD) 91 -- 5.5 Hilbert-Huang Transform (HHT) 94 -- 5.6 Wigner-Ville Distribution 96. 5.7 Local Mean Decomposition (LMD) 98 -- 5.8 Kurtosis and Kurtograms 100 -- 5.9 Summary 105 -- References 106 -- Part III Rotating Machine Condition Monitoring Using Machine Learning 115 -- 6 Vibration-Based Condition Monitoring Using Machine Learning 117 -- 6.1 Introduction 117 -- 6.2 Overview of the Vibration-Based MCM Process 118 -- 6.2.1 Fault-Detection and -Diagnosis Problem Framework 118 -- 6.3 Learning from Vibration Data 122 -- 6.3.1 Types of Learning 123 -- 6.3.1.1 Batch vs. Online Learning 123 -- 6.3.1.2 Instance-Based vs. Model-Based Learning 123 -- 6.3.1.3 Supervised Learning vs. Unsupervised Learning 123 -- 6.3.1.4 Semi-Supervised Learning 123 -- 6.3.1.5 Reinforcement Learning 124 -- 6.3.1.6 Transfer Learning 124 -- 6.3.2 Main Challenges of Learning from Vibration Data 125 -- 6.3.2.1 The Curse of Dimensionality 125 -- 6.3.2.2 Irrelevant Features 126 -- 6.3.2.3 Environment and Operating Conditions of a Rotating Machine 126 -- 6.3.3 Preparing Vibration Data for Analysis 126 -- 6.3.3.1 Normalisation 126 -- 6.3.3.2 Dimensionality Reduction 127 -- 6.4 Summary 128 -- References 128 -- 7 Linear Subspace Learning 131 -- 7.1 Introduction 131 -- 7.2 Principal Component Analysis (PCA) 132 -- 7.2.1 PCA Using Eigenvector Decomposition 132 -- 7.2.2 PCA Using SVD 133 -- 7.2.3 Application of PCA in Machine Fault Diagnosis 134 -- 7.3 Independent Component Analysis (ICA) 137 -- 7.3.1 Minimisation of Mutual Information 138 -- 7.3.2 Maximisation of the Likelihood 138 -- 7.3.3 Application of ICA in Machine Fault Diagnosis 139 -- 7.4 Linear Discriminant Analysis (LDA) 141 -- 7.4.1 Application of LDA in Machine Fault Diagnosis 142 -- 7.5 Canonical Correlation Analysis (CCA) 143 -- 7.6 Partial Least Squares (PLS) 145 -- 7.7 Summary 146 -- References 147 -- 8 Nonlinear Subspace Learning 153 -- 8.1 Introduction 153 -- 8.2 Kernel Principal Component Analysis (KPCA) 153 -- 8.2.1 Application of KPCA in Machine Fault Diagnosis 156 -- 8.3 Isometric Feature Mapping (ISOMAP) 156 -- 8.3.1 Application of ISOMAP in Machine Fault Diagnosis 158. 8.4 Diffusion Maps (DMs) and Diffusion Distances 159 -- 8.4.1 Application of DMs in Machine Fault Diagnosis 160 -- 8.5 Laplacian Eigenmap (LE) 161 -- 8.5.1 Application of the LE in Machine Fault Diagnosis 161 -- 8.6 Local Linear Embedding (LLE) 162 -- 8.6.1 Application of LLE in Machine Fault Diagnosis 163 -- 8.7 Hessian-Based LLE 163 -- 8.7.1 Application of HLLE in Machine Fault Diagnosis 164 -- 8.8 Local Tangent Space Alignment Analysis (LTSA) 165 -- 8.8.1 Application of LTSA in Machine Fault Diagnosis 165 -- 8.9 Maximum Variance Unfolding (MVU) 166 -- 8.9.1 Application of MVU in Machine Fault Diagnosis 167 -- 8.10 Stochastic Proximity Embedding (SPE) 168 -- 8.10.1 Application of SPE in Machine Fault Diagnosis 168 -- 8.11 Summary 169 -- References 170 -- 9 Feature Selection 173 -- 9.1 Introduction 173 -- 9.2 Filter Model-Based Feature Selection 175 -- 9.2.1 Fisher Score (FS) 176 -- 9.2.2 Laplacian Score (LS) 177 -- 9.2.3 Relief and Relief-F Algorithms 178 -- 9.2.3.1 Relief Algorithm 178 -- 9.2.3.2 Relief-F Algorithm 179 -- 9.2.4 Pearson Correlation Coefficient (PCC) 180 -- 9.2.5 Information Gain (IG) and Gain Ratio (GR) 180 -- 9.2.6 Mutual Information (MI) 181 -- 9.2.7 Chi-Squared (Chi-2) 181 -- 9.2.8 Wilcoxon Ranking 181 -- 9.2.9 Application of Feature Ranking in Machine Fault Diagnosis 182 -- 9.3 Wrapper ModeĺôBased Feature Subset Selection 185 -- 9.3.1 Sequential Selection Algorithms 185 -- 9.3.2 Heuristic-Based Selection Algorithms 185 -- 9.3.2.1 Ant Colony Optimisation (ACO) 185 -- 9.3.2.2 Genetic Algorithms (GAs) and Genetic Programming 187 -- 9.3.2.3 Particle Swarm Optimisation (PSO) 188 -- 9.3.3 Application of Wrapper ModeĺôBased Feature Subset Selection in Machine Fault Diagnosis 189 -- 9.4 Embedded ModeĺôBased Feature Selection 192 -- 9.5 Summary 193 -- References 194 -- Part IV Classification Algorithms 199 -- 10 Decision Trees and Random Forests 201 -- 10.1 Introduction 201 -- 10.2 Decision Trees 202 -- 10.2.1 Univariate Splitting Criteria 204 -- 10.2.1.1 Gini Index 205. 10.2.1.2 Information Gain 206 -- 10.2.1.3 Distance Measure 207 -- 10.2.1.4 Orthogonal Criterion (ORT) 207 -- 10.2.2 Multivariate Splitting Criteria 207 -- 10.2.3 Tree-Pruning Methods 208 -- 10.2.3.1 Error-Complexity Pruning 208 -- 10.2.3.2 Minimum-Error Pruning 209 -- 10.2.3.3 Reduced-Error Pruning 209 -- 10.2.3.4 Critical-Value Pruning 210 -- 10.2.3.5 Pessimistic Pruning 210 -- 10.2.3.6 Minimum Description Length (MDL) Pruning 210 -- 10.2.4 Decision Tree Inducers 211 -- 10.2.4.1 CART 211 -- 10.2.4.2 ID3 211 -- 10.2.4.3 C4.5 211 -- 10.2.4.4 CHAID 212 -- 10.3 Decision Forests 212 -- 10.4 Application of Decision Trees/Forests in Machine Fault Diagnosis 213 -- 10.5 Summary 217 -- References 217 -- 11 Probabilistic Classification Methods 225 -- 11.1 Introduction 225 -- 11.2 Hidden Markov Model 225 -- 11.2.1 Application of Hidden Markov Models in Machine Fault Diagnosis 228 -- 11.3 Logistic Regression Model 230 -- 11.3.1 Logistic Regression Regularisation 232 -- 11.3.2 Multinomial Logistic Regression Model (MLR) 232 -- 11.3.3 Application of Logistic Regression in Machine Fault Diagnosis 233 -- 11.4 Summary 234 -- References 235 -- 12 Artificial Neural Networks (ANNs) 239 -- 12.1 Introduction 239 -- 12.2 Neural Network Basic Principles 240 -- 12.2.1 The Multilayer Perceptron 241 -- 12.2.2 The Radial Basis Function Network 243 -- 12.2.3 The Kohonen Network 244 -- 12.3 Application of Artificial Neural Networks in Machine Fault Diagnosis 245 -- 12.4 Summary 253 -- References 254 -- 13 Support Vector Machines (SVMs) 259 -- 13.1 Introduction 259 -- 13.2 Multiclass SVMs 262 -- 13.3 Selection of Kernel Parameters 263 -- 13.4 Application of SVMs in Machine Fault Diagnosis 263 -- 13.5 Summary 274 -- References 274 -- 14 Deep Learning 279 -- 14.1 Introduction 279 -- 14.2 Autoencoders 280 -- 14.3 Convolutional Neural Networks (CNNs) 283 -- 14.4 Deep Belief Networks (DBNs) 284 -- 14.5 Recurrent Neural Networks (RNNs) 285 -- 14.6 Overview of Deep Learning in MCM 286 -- 14.6.1 Application of AE-based DNNs in Machine Fault Diagnosis 286. 14.6.2 Application of CNNs in Machine Fault Diagnosis 292 -- 14.6.3 Application of DBNs in Machine Fault Diagnosis 296 -- 14.6.4 Application of RNNs in Machine Fault Diagnosis 298 -- 14.7 Summary 299 -- References 301 -- 15 Classification Algorithm Validation 307 -- 15.1 Introduction 307 -- 15.2 The Hold-Out Technique 308 -- 15.2.1 Three-Way Data Split 309 -- 15.3 Random Subsampling 309 -- 15.4 K-Fold Cross-Validation 310 -- 15.5 Leave-One-Out Cross-Validation 311 -- 15.6 Bootstrapping 311 -- 15.7 Overall Classification Accuracy 312 -- 15.8 Confusion Matrix 313 -- 15.9 Recall and Precision 314 -- 15.10 ROC Graphs 315 -- 15.11 Summary 317 -- References 318 -- Part V New Fault Diagnosis Frameworks Designed for MCM 321 -- 16 Compressive Sampling and Subspace Learning (CS-SL) 323 -- 16.1 Introduction 323 -- 16.2 Compressive Sampling for Vibration-Based MCM 325 -- 16.2.1 Compressive Sampling Basics 325 -- 16.2.2 CS for Sparse Frequency Representation 328 -- 16.2.3 CS for Sparse Time-Frequency Representation 329 -- 16.3 Overview of CS in Machine Condition Monitoring 330 -- 16.3.1 Compressed Sensed Data Followed by Complete Data Construction 330 -- 16.3.2 Compressed Sensed Data Followed by Incomplete Data Construction 331 -- 16.3.3 Compressed Sensed Data as the Input of a Classifier 332 -- 16.3.4 Compressed Sensed Data Followed by Feature Learning 333 -- 16.4 Compressive Sampling and Feature Ranking (CS-FR) 333 -- 16.4.1 Implementations 334 -- 16.4.1.1 CS-LS 336 -- 16.4.1.2 CS-FS 336 -- 16.4.1.3 CS-Relief-F 337 -- 16.4.1.4 CS-PCC 338 -- 16.4.1.5 CS-Chi-2 338 -- 16.5 CS and Linear Subspace Learning-Based Framework for Fault Diagnosis 339 -- 16.5.1 Implementations 339 -- 16.5.1.1 CS-PCA 339 -- 16.5.1.2 CS-LDA 340 -- 16.5.1.3 CS-CPDC 341 -- 16.6 CS and Nonlinear Subspace Learning-Based Framework for Fault Diagnosis 343 -- 16.6.1 Implementations 344 -- 16.6.1.1 CS-KPCA 344 -- 16.6.1.2 CS-KLDA 345 -- 16.6.1.3 CS-CMDS 346 -- 16.6.1.4 CS-SPE 346 -- 16.7 Applications 348 -- 16.7.1 Case Study 1 348. 16.7.1.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 350 -- 16.7.1.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 352 -- 16.7.2 Case Study 2 354 -- 16.7.2.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 354 -- 16.7.2.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 355 -- 16.8 Discussion 355 -- References 357 -- 17 Compressive Sampling and Deep Neural Network (CS-DNN) 361 -- 17.1 Introduction 361 -- 17.2 Related Work 361 -- 17.3 CS-SAE-DNN 362 -- 17.3.1 Compressed Measurements Generation 362 -- 17.3.2 CS Model Testing Using the Flip Test 363 -- 17.3.3 DNN-Based Unsupervised Sparse Overcomplete Feature Learning 363 -- 17.3.4 Supervised Fine Tuning 367 -- 17.4 Applications 367 -- 17.4.1 Case Study 1 367 -- 17.4.2 Case Study 2 372 -- 17.5 Discussion 375 -- References 375 -- 18 Conclusion 379 -- 18.1 Introduction 379 -- 18.2 Summary and Conclusion 380 -- Appendix Machinery Vibration Data Resources and Analysis Algorithms 389 -- References 394 -- Index 395. |
| Record Nr. | UNINA-9910555291703321 |
|
Ahmed Hosameldin <1976->
|
||
| Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi
| Condition monitoring with vibration signals : compressive sampling and learning algorithms for rotating machines / / Hosameldin Ahmed and Asoke K. Nandi |
| Autore | Ahmed Hosameldin <1976-> |
| Pubbl/distr/stampa | Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020 |
| Descrizione fisica | 1 online resource (437 pages) |
| Disciplina | 621.80287 |
| Soggetto topico | Machinery - Monitoring |
| ISBN |
1-119-54464-5
1-119-54467-X 1-119-54463-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xvii -- About the Authors xxi -- List of Abbreviations xxiii -- Part I Introduction 1 -- 1 Introduction to Machine Condition Monitoring 3 -- 1.1 Background 3 -- 1.2 Maintenance Approaches for Rotating Machines Failures 4 -- 1.2.1 Corrective Maintenance 4 -- 1.2.2 Preventive Maintenance 5 -- 1.2.2.1 Time-Based Maintenance (TBM) 5 -- 1.2.2.2 Condition-Based Maintenance (CBM) 5 -- 1.3 Applications of MCM 5 -- 1.3.1 Wind Turbines 5 -- 1.3.2 Oil and Gas 6 -- 1.3.3 Aerospace and Defence Industry 6 -- 1.3.4 Automotive 7 -- 1.3.5 Marine Engines 7 -- 1.3.6 Locomotives 7 -- 1.4 Condition Monitoring Techniques 7 -- 1.4.1 Vibration Monitoring 7 -- 1.4.2 Acoustic Emission 8 -- 1.4.3 Fusion of Vibration and Acoustic 8 -- 1.4.4 Motor Current Monitoring 8 -- 1.4.5 Oil Analysis and Lubrication Monitoring 8 -- 1.4.6 Thermography 9 -- 1.4.7 Visual Inspection 9 -- 1.4.8 Performance Monitoring 9 -- 1.4.9 Trend Monitoring 10 -- 1.5 Topic Overview and Scope of the Book 10 -- 1.6 Summary 11 -- References 11 -- 2 Principles of Rotating Machine Vibration Signals 17 -- 2.1 Introduction 17 -- 2.2 Machine Vibration Principles 17 -- 2.3 Sources of Rotating Machines Vibration Signals 20 -- 2.3.1 Rotor Mass Unbalance 21 -- 2.3.2 Misalignment 21 -- 2.3.3 Cracked Shafts 21 -- 2.3.4 Rolling Element Bearings 23 -- 2.3.5 Gears 25 -- 2.4 Types of Vibration Signals 25 -- 2.4.1 Stationary 26 -- 2.4.2 Nonstationary 26 -- 2.5 Vibration Signal Acquisition 26 -- 2.5.1 Displacement Transducers 26 -- 2.5.2 Velocity Transducers 26 -- 2.5.3 Accelerometers 27 -- 2.6 Advantages and Limitations of Vibration Signal Monitoring 27 -- 2.7 Summary 28 -- References 28 -- Part II Vibration Signal Analysis Techniques 31 -- 3 Time Domain Analysis 33 -- 3.1 Introduction 33 -- 3.1.1 Visual Inspection 33 -- 3.1.2 Features-Based Inspection 35 -- 3.2 Statistical Functions 35 -- 3.2.1 Peak Amplitude 36 -- 3.2.2 Mean Amplitude 36 -- 3.2.3 Root Mean Square Amplitude 36 -- 3.2.4 Peak-to-Peak Amplitude 36 -- 3.2.5 Crest Factor (CF) 36.
3.2.6 Variance and Standard Deviation 37 -- 3.2.7 Standard Error 37 -- 3.2.8 Zero Crossing 38 -- 3.2.9 Wavelength 39 -- 3.2.10 Willison Amplitude 39 -- 3.2.11 Slope Sign Change 39 -- 3.2.12 Impulse Factor 39 -- 3.2.13 Margin Factor 40 -- 3.2.14 Shape Factor 40 -- 3.2.15 Clearance Factor 40 -- 3.2.16 Skewness 40 -- 3.2.17 Kurtosis 40 -- 3.2.18 Higher-Order Cumulants (HOCs) 41 -- 3.2.19 Histograms 42 -- 3.2.20 Normal/Weibull Negative Log-Likelihood Value 42 -- 3.2.21 Entropy 42 -- 3.3 Time Synchronous Averaging 44 -- 3.3.1 TSA Signals 44 -- 3.3.2 Residual Signal (RES) 44 -- 3.3.2.1 NA4 44 -- 3.3.2.2 NA4* 45 -- 3.3.3 Difference Signal (DIFS) 45 -- 3.3.3.1 FM4 46 -- 3.3.3.2 M6A 46 -- 3.3.3.3 M8A 46 -- 3.4 Time Series Regressive Models 46 -- 3.4.1 AR Model 47 -- 3.4.2 MA Model 48 -- 3.4.3 ARMA Model 48 -- 3.4.4 ARIMA Model 48 -- 3.5 Filter-Based Methods 49 -- 3.5.1 Demodulation 49 -- 3.5.2 Prony Model 52 -- 3.5.3 Adaptive Noise Cancellation (ANC) 53 -- 3.6 Stochastic Parameter Techniques 54 -- 3.7 Blind Source Separation (BSS) 54 -- 3.8 Summary 55 -- References 56 -- 4 Frequency Domain Analysis 63 -- 4.1 Introduction 63 -- 4.2 Fourier Analysis 64 -- 4.2.1 Fourier Series 64 -- 4.2.2 Discrete Fourier Transform 66 -- 4.2.3 Fast Fourier Transform (FFT) 67 -- 4.3 Envelope Analysis 71 -- 4.4 Frequency Spectrum Statistical Features 73 -- 4.4.1 Arithmetic Mean 73 -- 4.4.2 Geometric Mean 73 -- 4.4.3 Matched Filter RMS 73 -- 4.4.4 The RMS of Spectral Difference 74 -- 4.4.5 The Sum of Squares Spectral Difference 74 -- 4.4.6 High-Order Spectra Techniques 74 -- 4.5 Summary 75 -- References 76 -- 5 Time-Frequency Domain Analysis 79 -- 5.1 Introduction 79 -- 5.2 Short-Time Fourier Transform (STFT) 79 -- 5.3 Wavelet Analysis 82 -- 5.3.1 Wavelet Transform (WT) 82 -- 5.3.1.1 Continuous Wavelet Transform (CWT) 83 -- 5.3.1.2 Discrete Wavelet Transform (DWT) 85 -- 5.3.2 Wavelet Packet Transform (WPT) 89 -- 5.4 Empirical Mode Decomposition (EMD) 91 -- 5.5 Hilbert-Huang Transform (HHT) 94 -- 5.6 Wigner-Ville Distribution 96. 5.7 Local Mean Decomposition (LMD) 98 -- 5.8 Kurtosis and Kurtograms 100 -- 5.9 Summary 105 -- References 106 -- Part III Rotating Machine Condition Monitoring Using Machine Learning 115 -- 6 Vibration-Based Condition Monitoring Using Machine Learning 117 -- 6.1 Introduction 117 -- 6.2 Overview of the Vibration-Based MCM Process 118 -- 6.2.1 Fault-Detection and -Diagnosis Problem Framework 118 -- 6.3 Learning from Vibration Data 122 -- 6.3.1 Types of Learning 123 -- 6.3.1.1 Batch vs. Online Learning 123 -- 6.3.1.2 Instance-Based vs. Model-Based Learning 123 -- 6.3.1.3 Supervised Learning vs. Unsupervised Learning 123 -- 6.3.1.4 Semi-Supervised Learning 123 -- 6.3.1.5 Reinforcement Learning 124 -- 6.3.1.6 Transfer Learning 124 -- 6.3.2 Main Challenges of Learning from Vibration Data 125 -- 6.3.2.1 The Curse of Dimensionality 125 -- 6.3.2.2 Irrelevant Features 126 -- 6.3.2.3 Environment and Operating Conditions of a Rotating Machine 126 -- 6.3.3 Preparing Vibration Data for Analysis 126 -- 6.3.3.1 Normalisation 126 -- 6.3.3.2 Dimensionality Reduction 127 -- 6.4 Summary 128 -- References 128 -- 7 Linear Subspace Learning 131 -- 7.1 Introduction 131 -- 7.2 Principal Component Analysis (PCA) 132 -- 7.2.1 PCA Using Eigenvector Decomposition 132 -- 7.2.2 PCA Using SVD 133 -- 7.2.3 Application of PCA in Machine Fault Diagnosis 134 -- 7.3 Independent Component Analysis (ICA) 137 -- 7.3.1 Minimisation of Mutual Information 138 -- 7.3.2 Maximisation of the Likelihood 138 -- 7.3.3 Application of ICA in Machine Fault Diagnosis 139 -- 7.4 Linear Discriminant Analysis (LDA) 141 -- 7.4.1 Application of LDA in Machine Fault Diagnosis 142 -- 7.5 Canonical Correlation Analysis (CCA) 143 -- 7.6 Partial Least Squares (PLS) 145 -- 7.7 Summary 146 -- References 147 -- 8 Nonlinear Subspace Learning 153 -- 8.1 Introduction 153 -- 8.2 Kernel Principal Component Analysis (KPCA) 153 -- 8.2.1 Application of KPCA in Machine Fault Diagnosis 156 -- 8.3 Isometric Feature Mapping (ISOMAP) 156 -- 8.3.1 Application of ISOMAP in Machine Fault Diagnosis 158. 8.4 Diffusion Maps (DMs) and Diffusion Distances 159 -- 8.4.1 Application of DMs in Machine Fault Diagnosis 160 -- 8.5 Laplacian Eigenmap (LE) 161 -- 8.5.1 Application of the LE in Machine Fault Diagnosis 161 -- 8.6 Local Linear Embedding (LLE) 162 -- 8.6.1 Application of LLE in Machine Fault Diagnosis 163 -- 8.7 Hessian-Based LLE 163 -- 8.7.1 Application of HLLE in Machine Fault Diagnosis 164 -- 8.8 Local Tangent Space Alignment Analysis (LTSA) 165 -- 8.8.1 Application of LTSA in Machine Fault Diagnosis 165 -- 8.9 Maximum Variance Unfolding (MVU) 166 -- 8.9.1 Application of MVU in Machine Fault Diagnosis 167 -- 8.10 Stochastic Proximity Embedding (SPE) 168 -- 8.10.1 Application of SPE in Machine Fault Diagnosis 168 -- 8.11 Summary 169 -- References 170 -- 9 Feature Selection 173 -- 9.1 Introduction 173 -- 9.2 Filter Model-Based Feature Selection 175 -- 9.2.1 Fisher Score (FS) 176 -- 9.2.2 Laplacian Score (LS) 177 -- 9.2.3 Relief and Relief-F Algorithms 178 -- 9.2.3.1 Relief Algorithm 178 -- 9.2.3.2 Relief-F Algorithm 179 -- 9.2.4 Pearson Correlation Coefficient (PCC) 180 -- 9.2.5 Information Gain (IG) and Gain Ratio (GR) 180 -- 9.2.6 Mutual Information (MI) 181 -- 9.2.7 Chi-Squared (Chi-2) 181 -- 9.2.8 Wilcoxon Ranking 181 -- 9.2.9 Application of Feature Ranking in Machine Fault Diagnosis 182 -- 9.3 Wrapper ModeĺôBased Feature Subset Selection 185 -- 9.3.1 Sequential Selection Algorithms 185 -- 9.3.2 Heuristic-Based Selection Algorithms 185 -- 9.3.2.1 Ant Colony Optimisation (ACO) 185 -- 9.3.2.2 Genetic Algorithms (GAs) and Genetic Programming 187 -- 9.3.2.3 Particle Swarm Optimisation (PSO) 188 -- 9.3.3 Application of Wrapper ModeĺôBased Feature Subset Selection in Machine Fault Diagnosis 189 -- 9.4 Embedded ModeĺôBased Feature Selection 192 -- 9.5 Summary 193 -- References 194 -- Part IV Classification Algorithms 199 -- 10 Decision Trees and Random Forests 201 -- 10.1 Introduction 201 -- 10.2 Decision Trees 202 -- 10.2.1 Univariate Splitting Criteria 204 -- 10.2.1.1 Gini Index 205. 10.2.1.2 Information Gain 206 -- 10.2.1.3 Distance Measure 207 -- 10.2.1.4 Orthogonal Criterion (ORT) 207 -- 10.2.2 Multivariate Splitting Criteria 207 -- 10.2.3 Tree-Pruning Methods 208 -- 10.2.3.1 Error-Complexity Pruning 208 -- 10.2.3.2 Minimum-Error Pruning 209 -- 10.2.3.3 Reduced-Error Pruning 209 -- 10.2.3.4 Critical-Value Pruning 210 -- 10.2.3.5 Pessimistic Pruning 210 -- 10.2.3.6 Minimum Description Length (MDL) Pruning 210 -- 10.2.4 Decision Tree Inducers 211 -- 10.2.4.1 CART 211 -- 10.2.4.2 ID3 211 -- 10.2.4.3 C4.5 211 -- 10.2.4.4 CHAID 212 -- 10.3 Decision Forests 212 -- 10.4 Application of Decision Trees/Forests in Machine Fault Diagnosis 213 -- 10.5 Summary 217 -- References 217 -- 11 Probabilistic Classification Methods 225 -- 11.1 Introduction 225 -- 11.2 Hidden Markov Model 225 -- 11.2.1 Application of Hidden Markov Models in Machine Fault Diagnosis 228 -- 11.3 Logistic Regression Model 230 -- 11.3.1 Logistic Regression Regularisation 232 -- 11.3.2 Multinomial Logistic Regression Model (MLR) 232 -- 11.3.3 Application of Logistic Regression in Machine Fault Diagnosis 233 -- 11.4 Summary 234 -- References 235 -- 12 Artificial Neural Networks (ANNs) 239 -- 12.1 Introduction 239 -- 12.2 Neural Network Basic Principles 240 -- 12.2.1 The Multilayer Perceptron 241 -- 12.2.2 The Radial Basis Function Network 243 -- 12.2.3 The Kohonen Network 244 -- 12.3 Application of Artificial Neural Networks in Machine Fault Diagnosis 245 -- 12.4 Summary 253 -- References 254 -- 13 Support Vector Machines (SVMs) 259 -- 13.1 Introduction 259 -- 13.2 Multiclass SVMs 262 -- 13.3 Selection of Kernel Parameters 263 -- 13.4 Application of SVMs in Machine Fault Diagnosis 263 -- 13.5 Summary 274 -- References 274 -- 14 Deep Learning 279 -- 14.1 Introduction 279 -- 14.2 Autoencoders 280 -- 14.3 Convolutional Neural Networks (CNNs) 283 -- 14.4 Deep Belief Networks (DBNs) 284 -- 14.5 Recurrent Neural Networks (RNNs) 285 -- 14.6 Overview of Deep Learning in MCM 286 -- 14.6.1 Application of AE-based DNNs in Machine Fault Diagnosis 286. 14.6.2 Application of CNNs in Machine Fault Diagnosis 292 -- 14.6.3 Application of DBNs in Machine Fault Diagnosis 296 -- 14.6.4 Application of RNNs in Machine Fault Diagnosis 298 -- 14.7 Summary 299 -- References 301 -- 15 Classification Algorithm Validation 307 -- 15.1 Introduction 307 -- 15.2 The Hold-Out Technique 308 -- 15.2.1 Three-Way Data Split 309 -- 15.3 Random Subsampling 309 -- 15.4 K-Fold Cross-Validation 310 -- 15.5 Leave-One-Out Cross-Validation 311 -- 15.6 Bootstrapping 311 -- 15.7 Overall Classification Accuracy 312 -- 15.8 Confusion Matrix 313 -- 15.9 Recall and Precision 314 -- 15.10 ROC Graphs 315 -- 15.11 Summary 317 -- References 318 -- Part V New Fault Diagnosis Frameworks Designed for MCM 321 -- 16 Compressive Sampling and Subspace Learning (CS-SL) 323 -- 16.1 Introduction 323 -- 16.2 Compressive Sampling for Vibration-Based MCM 325 -- 16.2.1 Compressive Sampling Basics 325 -- 16.2.2 CS for Sparse Frequency Representation 328 -- 16.2.3 CS for Sparse Time-Frequency Representation 329 -- 16.3 Overview of CS in Machine Condition Monitoring 330 -- 16.3.1 Compressed Sensed Data Followed by Complete Data Construction 330 -- 16.3.2 Compressed Sensed Data Followed by Incomplete Data Construction 331 -- 16.3.3 Compressed Sensed Data as the Input of a Classifier 332 -- 16.3.4 Compressed Sensed Data Followed by Feature Learning 333 -- 16.4 Compressive Sampling and Feature Ranking (CS-FR) 333 -- 16.4.1 Implementations 334 -- 16.4.1.1 CS-LS 336 -- 16.4.1.2 CS-FS 336 -- 16.4.1.3 CS-Relief-F 337 -- 16.4.1.4 CS-PCC 338 -- 16.4.1.5 CS-Chi-2 338 -- 16.5 CS and Linear Subspace Learning-Based Framework for Fault Diagnosis 339 -- 16.5.1 Implementations 339 -- 16.5.1.1 CS-PCA 339 -- 16.5.1.2 CS-LDA 340 -- 16.5.1.3 CS-CPDC 341 -- 16.6 CS and Nonlinear Subspace Learning-Based Framework for Fault Diagnosis 343 -- 16.6.1 Implementations 344 -- 16.6.1.1 CS-KPCA 344 -- 16.6.1.2 CS-KLDA 345 -- 16.6.1.3 CS-CMDS 346 -- 16.6.1.4 CS-SPE 346 -- 16.7 Applications 348 -- 16.7.1 Case Study 1 348. 16.7.1.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 350 -- 16.7.1.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 352 -- 16.7.2 Case Study 2 354 -- 16.7.2.1 The Combination of MMV-CS and Several Feature-Ranking Techniques 354 -- 16.7.2.2 The Combination of MMV-CS and Several Linear and Nonlinear Subspace Learning Techniques 355 -- 16.8 Discussion 355 -- References 357 -- 17 Compressive Sampling and Deep Neural Network (CS-DNN) 361 -- 17.1 Introduction 361 -- 17.2 Related Work 361 -- 17.3 CS-SAE-DNN 362 -- 17.3.1 Compressed Measurements Generation 362 -- 17.3.2 CS Model Testing Using the Flip Test 363 -- 17.3.3 DNN-Based Unsupervised Sparse Overcomplete Feature Learning 363 -- 17.3.4 Supervised Fine Tuning 367 -- 17.4 Applications 367 -- 17.4.1 Case Study 1 367 -- 17.4.2 Case Study 2 372 -- 17.5 Discussion 375 -- References 375 -- 18 Conclusion 379 -- 18.1 Introduction 379 -- 18.2 Summary and Conclusion 380 -- Appendix Machinery Vibration Data Resources and Analysis Algorithms 389 -- References 394 -- Index 395. |
| Record Nr. | UNINA-9910813338803321 |
|
Ahmed Hosameldin <1976->
|
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| Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
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. | UNINA-9910552984603321 |
| Warszawa, Poland : , : Polskie Towarzystwo Diagnostyki Technicznej | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical hazops, trips and alarms [[electronic resource] /] / David Macdonald
| Practical hazops, trips and alarms [[electronic resource] /] / David Macdonald |
| Autore | Macdonald Dave <1942-> |
| Pubbl/distr/stampa | Oxford, : Newnes, 2004 |
| Descrizione fisica | 1 online resource (345 p.) |
| Disciplina | 621.30289 |
| Collana | Practical professional books from Elsevier |
| Soggetto topico |
Machinery - Safety appliances
Machinery - Monitoring |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-00931-8
9786611009311 0-08-048019-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Introduction to the book; 1. Introduction to hazard studies; 1.1 Scope and objectives of this chapter; 1.2 Introduction to hazards and risk management; 1.3 Risk assessment; 1.4 Concepts of Alarp and tolerable risk; 1.5 Regulatory frameworks and examples from EU and USA; 1.6 Methods of identifying hazards; 2. Hazard studies at levels 1 and 2; Objectives 2; 2.1 Introduction; 2.2 Methodologies for hazard study 1; 2.3 Process hazard study 2; 2.4 Practical example of hazard 2 application; 2.5 Case study; 2.6 Conclusion on hazard studies 1 and 2
3. Risk reduction measures using alarms and trips3.1 Risk reduction measures; 3.2 Terminologies and standards for safety systems; 3.3 Equipment under control; 3.4 Protection layers; 3.5 The role of alarms in safety; 3.6 Alarm types and do they qualify as safeguards?; 3.7 Identification and design of safety-related alarms; 3.8 Key design principles for alarms; 3.9 SIS, principles of separation; 3.10 Simple and complex shutdown sequences, examples; 3.11 Conclusions: the role of Hazops in defining alarms and trips; 4. Hazop method; Objectives 4; 4.1 Introduction; 4.2 Introduction to Hazop 4.3 Overview of Hazop method4.4 Points to note on the examination procedure; 4.5 Practical exercise: continuous process example; 4.6 Hazop for batch processes and sequential operations; 4.7 Hazops for other disciplines; 4.8 Conclusions; 5. Planning and leadership of Hazops; Objectives 5; 5.1 Introduction; 5.2 Organizing the Hazop; 5.3 The team leader and the team; 5.4 Practical exercise: hybrid batch process example; 6. Specifying safety instrumented systems; Objectives 6; 6.1 Introduction; 6.2 Risk reduction by instrumented protection 6.3 What affects the safety integrity of an instrument trip?6.4 Overview of IEC 61508; 6.5 Determining the safety integrity; 6.6 Design essentials to meet SIL targets; 6.7 Specifying the SIS requirements; 6.8 Documenting the SRS; 6.9 Conclusions; 7. Hazard analysis methods; 7.1 Introduction; 7.2 Outline of methods; 7.3 Fault tree analysis; 7.4 Practical exercise in FTA; 7.5 Conclusions; 8. Factors in the choice of protection system; 8.1 Introduction and objectives; 8.2 Equipment selection; 8.3 Key points about sensors and actuators 8.4 Guidelines for the application of field devices in the SIS8.5 IEC 61508 requirements for field devices; 8.6 Technology issues; 8.7 Guidelines for final elements; 8.8 Summary of technology and applications; 8.9 Summary of SIL vs cost; 9. Exercise in specifying an SIS from the Hazop; Objective 9; 9.1 Introduction; 9.2 Process description; 9.3 Safety requirements specifications; 9.4 Conclusion; Appendix A: References used in the manual; Appendix B: Some websites for safety systems information; Appendix C: Notes on national regulations relevant to hazard study and safety management Appendix D: Software tools for hazard studies |
| Record Nr. | UNINA-9910457937603321 |
|
Macdonald Dave <1942->
|
||
| Oxford, : Newnes, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Practical hazops, trips and alarms [[electronic resource] /] / David Macdonald
| Practical hazops, trips and alarms [[electronic resource] /] / David Macdonald |
| Autore | Macdonald Dave <1942-> |
| Pubbl/distr/stampa | Oxford, : Newnes, 2004 |
| Descrizione fisica | 1 online resource (345 p.) |
| Disciplina | 621.30289 |
| Collana | Practical professional books from Elsevier |
| Soggetto topico |
Machinery - Safety appliances
Machinery - Monitoring |
| ISBN |
1-281-00931-8
9786611009311 0-08-048019-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Preface; Introduction to the book; 1. Introduction to hazard studies; 1.1 Scope and objectives of this chapter; 1.2 Introduction to hazards and risk management; 1.3 Risk assessment; 1.4 Concepts of Alarp and tolerable risk; 1.5 Regulatory frameworks and examples from EU and USA; 1.6 Methods of identifying hazards; 2. Hazard studies at levels 1 and 2; Objectives 2; 2.1 Introduction; 2.2 Methodologies for hazard study 1; 2.3 Process hazard study 2; 2.4 Practical example of hazard 2 application; 2.5 Case study; 2.6 Conclusion on hazard studies 1 and 2
3. Risk reduction measures using alarms and trips3.1 Risk reduction measures; 3.2 Terminologies and standards for safety systems; 3.3 Equipment under control; 3.4 Protection layers; 3.5 The role of alarms in safety; 3.6 Alarm types and do they qualify as safeguards?; 3.7 Identification and design of safety-related alarms; 3.8 Key design principles for alarms; 3.9 SIS, principles of separation; 3.10 Simple and complex shutdown sequences, examples; 3.11 Conclusions: the role of Hazops in defining alarms and trips; 4. Hazop method; Objectives 4; 4.1 Introduction; 4.2 Introduction to Hazop 4.3 Overview of Hazop method4.4 Points to note on the examination procedure; 4.5 Practical exercise: continuous process example; 4.6 Hazop for batch processes and sequential operations; 4.7 Hazops for other disciplines; 4.8 Conclusions; 5. Planning and leadership of Hazops; Objectives 5; 5.1 Introduction; 5.2 Organizing the Hazop; 5.3 The team leader and the team; 5.4 Practical exercise: hybrid batch process example; 6. Specifying safety instrumented systems; Objectives 6; 6.1 Introduction; 6.2 Risk reduction by instrumented protection 6.3 What affects the safety integrity of an instrument trip?6.4 Overview of IEC 61508; 6.5 Determining the safety integrity; 6.6 Design essentials to meet SIL targets; 6.7 Specifying the SIS requirements; 6.8 Documenting the SRS; 6.9 Conclusions; 7. Hazard analysis methods; 7.1 Introduction; 7.2 Outline of methods; 7.3 Fault tree analysis; 7.4 Practical exercise in FTA; 7.5 Conclusions; 8. Factors in the choice of protection system; 8.1 Introduction and objectives; 8.2 Equipment selection; 8.3 Key points about sensors and actuators 8.4 Guidelines for the application of field devices in the SIS8.5 IEC 61508 requirements for field devices; 8.6 Technology issues; 8.7 Guidelines for final elements; 8.8 Summary of technology and applications; 8.9 Summary of SIL vs cost; 9. Exercise in specifying an SIS from the Hazop; Objective 9; 9.1 Introduction; 9.2 Process description; 9.3 Safety requirements specifications; 9.4 Conclusion; Appendix A: References used in the manual; Appendix B: Some websites for safety systems information; Appendix C: Notes on national regulations relevant to hazard study and safety management Appendix D: Software tools for hazard studies |
| Record Nr. | UNINA-9910784462303321 |
|
Macdonald Dave <1942->
|
||
| Oxford, : Newnes, 2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
