Structural Health Monitoring (eBook)

Preface 6
Contents 8
About the Editors 10
1 Advanced Signal Processing for Structural Health Monitoring 13
Abstract 13
1 Introduction 13
2 Structural Health Monitoring 16
2.1 Operational Evaluation 16
2.2 Data Acquisition 17
2.3 Feature Extraction 18
2.4 Diagnosis and Prognosis 19
3 Signal Processing in SHM 20
References 21
2 Signal Post-processing for Accurate Evaluation of the Natural Frequencies 24
Abstract 24
1 Introduction 24
2 Motivation 25
3 Standard Frequency Evaluation 26
4 Simple Methods to Improve the Frequency Readability 31
5 Description and Implementation of the Iterative Algorithm 40
6 Testing the Algorithm Efficiency 44
7 Conclusions 47
Acknowledgements 47
References 47
3 Holobalancing Method and Its Improvement by Reselection of Balancing Object 49
Abstract 49
1 Introduction 49
2 Construction of Holospectrum 50
2.1 Basic Condition Required 50
2.2 Three-Dimensional Holospectrum (3dH) 52
3 Introduction of Holobalancing Method 55
3.1 Initial Phase Point (IPP) 55
3.2 Precession Angle Compensation 57
3.3 Differential Holospectrum and Transfer Matrix 58
3.4 The Balancing Procedure 59
4 Balancing Object Reselection 61
4.1 Characteristic and Deficiency of the IPV 61
4.2 Precession Decomposition 63
4.3 Balancing Object Selection: Characteristic Analysis of IPV+ and IPV? [7] 64
5 Experimental Verification and Case Study 68
5.1 Experimental Verification 68
5.2 Case Study 70
6 Conclusion and Discussion 72
References 73
4 Wavelet Transform Based on Inner Product for Fault Diagnosis of Rotating Machinery 74
Abstract 74
1 Introduction 74
2 Wavelet Transform Based on Inner Product 77
2.1 Inner Product 77
2.2 CWT, DWT and WPT 78
2.3 Inner Product Validation of WT in RMFD 80
3 Adaptive Multiwavelet for RMFD 86
3.1 Summary of Multiwavelet Theory 86
3.2 Adaptive Multiwavelet Construction 89
3.3 Experimental Study 91
4 Discussion 97
5 Conclusion 98
References 98
5 Wavelet Based Spectral Kurtosis and Kurtogram: A Smart and Sparse Characterization of Impulsive Transient Vibration 101
Abstract 101
1 A Brief Introduction 102
2 Spectral Kurtosis and Fast Kurtogram 103
2.1 Signal Modelling 103
2.2 Spectral Kurtosis 105
2.3 Illustration Example of Spectral Kurtosis 106
3 Wavelet Based Kurtogram and Its Development 108
3.1 STFT Based Kurtogram 109
3.2 Fast Kurtogram 110
3.3 Wavelet Packet Based Kurtogram 110
4 Wavelet Tight Frame Based Kurtogram 111
4.1 Limitation of Original Kurtogram 111
4.2 Quasi-Analytic Wavelet Tight Frame 112
4.3 Spatial-Spectral Ensemble Kurtosis and Its Kurtogram 116
4.4 Numerical Simulations and Engineering Applications 118
5 Adaptive Super-Wavelet Based Kurtogram 125
5.1 Adaptive Super-Wavelet Transform 125
5.2 A Sparse Indictor: Fault Feature Ratio (FFR) 128
5.3 Adaptive ESW Based Kurtogram 129
5.4 Engineering Applications 133
6 Conclusions 136
Acknowledgements 136
References 136
6 Time-Frequency Manifold for Machinery Fault Diagnosis 139
Abstract 139
1 Introduction 139
2 Time-Frequency Manifold Analysis 141
2.1 Principle 141
2.2 Phase Space Reconstruction 143
2.3 Time-Frequency Distribution 143
2.4 TFM Learning 145
2.5 Procedure of Time-Frequency Manifold Analysis 147
3 Time-Frequency Manifold Synthesis 148
3.1 Principle 148
3.2 TFD Re-Generation 149
3.3 Time-Frequency Synthesis 149
3.4 PSR Synthesis 150
3.5 Procedure of TFM Synthesis 151
4 Experiments for Machinery Fault Diagnosis 152
4.1 Gear Fault Diagnosis 152
4.2 Bearing Defect Diagnosis 156
5 Conclusions 160
References 161
7 Matching Demodulation Transform and Its Application in Machine Fault Diagnosis 163
Abstract 163
1 Introduction 163
2 Theoretical Background 166
2.1 Short-Time Fourier Transform 167
2.2 Performance Analysis of the IF Estimator 168
3 Matching Demodulation Transform 169
3.1 Matching Demodulation Transform for Mono-Component Signal 170
3.2 Matching Demodulation Transform for Multicomponent Signal 172
3.3 Practical Iterative Implementation of Matching Demodulation Transform 176
4 Performance Analysis of Matching Demodulation Transform 180
4.1 Quantitative Analysis of IF Estimation Error 180
4.2 Convergence Condition and Discussion 187
5 Simulation Study 189
5.1 Applying the MDT to Simulation Signal 189
5.2 Applying Signal Reconstruction to Simulation Signal 196
6 Experimental Verification 198
7 Applications 202
8 Conclusions 207
Acknowledgements 207
References 207
8 Compressive Sensing: A New Insight to Condition Monitoring of Rotary Machinery 211
Abstract 211
1 Introduction 211
2 Problem Statement 214
3 Compressive Sensing Theory 215
3.1 Shannon’s Sampling Theory 215
3.2 Compressive Sensing 216
3.3 Sparse Representation of a Signal 217
3.4 Sampling Method 218
3.5 Optimization Solving Strategy 219
4 Proposed Strategies and Applications 219
4.1 Experiments 219
4.2 Reconstruction of Incomplete Vibration Signal 220
4.3 Fault Classification of Rotating Machinery [25] 224
4.4 Compressive Sensing of Bearing Fault via Characteristic Harmonic Detection 228
5 Conclusions 231
References 232
9 Sparse Representation of the Transients in Mechanical Signals 234
Abstract 234
1 Introduction 234
2 Sparse Representation Theory 235
2.1 Sparse Representation Model 235
2.2 Construction of the Over-Complete Dictionary 237
2.3 Solution to Sparse Representation Model 238
3 Over-Complete Wavelet Basis Dictionary 240
3.1 General Over-Complete Wavelet Basis Dictionary 240
3.2 Correlation Filtering 242
4 Solution to Representation Coefficients Based on BPDN 242
4.1 Data Fidelity Optimization 242
4.2 Penalty Optimization 244
5 Applications 246
5.1 Application in Gearbox Transient Feature Extraction 246
5.2 Application in Bearing Transient Feature Extraction 251
5.3 Application in Compound Fault Feature Extraction 258
6 Discussions 263
References 264
10 Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition 266
Abstract 266
1 Introduction 266
2 Empirical Mode Decomposition 267
2.1 EMD Algorithm 267
2.2 Problems of EMD 271
2.3 Hilbert-Huang Transform 273
3 Improved EMD Methods 274
3.1 EEMD Method 274
3.2 AEEMD Method 278
3.3 CEEMDAN Method 281
4 Fault Diagnosis of Rotating Machinery Using EMD Based Methods 285
4.1 Fault Diagnosis of Rotors 285
4.2 Fault Diagnosis of Gears 289
4.3 Fault Diagnosis of Rolling Element Bearings 292
5 Conclusions 296
References 297
11 Bivariate Empirical Mode Decomposition and Its Applications in Machine Condition Monitoring 300
Abstract 300
1 Introduction 300
2 Empirical Mode Decomposition 301
3 Bivariate Empirical Mode Decomposition 304
4 Applications of the BEMD in Machine Condition Monitoring 306
4.1 The CM of Bearing-Shaft Systems 306
4.2 The CM of Wind Turbines 314
5 Concluding Remarks 325
References 326
12 Time-Frequency Demodulation Analysis Based on LMD and Its Applications 327
Abstract 327
1 Introduction 327
2 Instantaneous Features of a Signal 328
3 A Brief Introduction of LMD 330
4 Some Key Issues of LMD 333
4.1 Boundary Processing 333
4.2 Determination of the Step Size of MA 336
4.3 Instantaneous Time-Frequency Spectrum 337
5 Time-Frequency Demodulation Analysis for the Simulated Signals 338
5.1 AM Signal Demodulation 339
5.2 FM Signal Demodulation 340
5.3 AM-FM Signal Demodulation 342
6 Applications 343
6.1 Rub Fault Detection in a Rotor System 343
6.2 Practical Rub-Impact Fault Diagnosis 344
6.3 Demodulation Analysis of a Gearbox 347
7 Conclusions 350
Acknowledgements 350
References 350
13 On the Use of Stochastic Resonance in Mechanical Fault Signal Detection 352
Abstract 352
1 Introduction 352
2 Bistable Stochastic Resonance Model 354
3 Normalized Scale Transform 355
3.1 Basic Theory of Normalized Scale Transform 355
3.2 Simulation Result of Normalized Scale Transform 357
3.3 Application of Normalized Scale Transform 358
4 SR Circuit Module 363
4.1 Circuit Module 363
4.2 Simulated Experiment of Circuit Module 366
4.3 Application of Circuit Module 366
5 Multi-scale Bistable Array SR 368
5.1 Stochastic Resonance Effect in MSBA 368
5.2 Signal Detection and Numerical Simulation 373
5.3 Application for Machinery Fault Diagnosis 375
6 Conclusions 379
Acknowledgements 379
References 379