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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
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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->  
Hoboken, New Jersey, USA : , : John Wiley & Sons, Inc., , 2020
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