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- Spis treści
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Summary 7
Streszczenie 9
Acknowledgements 11
1. Introduction 12
1.1. SHM and similar research fields 12
1.2. Effects of varying environmental and operational conditions on SHM 13
1.3. Why cointegration has been applied to SHM 16
1.4. A review of cointegration-based approaches for SHM 20
1.5. Motivation and scope of this monograph 24
2. Stationarity and nonstationarity 27
2.1. Definitions and basic concepts 27
2.2. Time series and stationarity 29
2.3. Unit root tests 32
2.4. The Dickey-Fuller (DF) and augmented Dickey-Fuller (ADF) tests 35
3. Cointegration method 39
3.1. Introduction to cointegration 39
3.2. Cointegration and common trends 40
3.3. Testing for cointegration 42
3.4. Johansen’s cointegration procedure 43
3.5. Testing for stationarity 45
3.6. Example using the Weierstrass–Mandelbrot cosine fractal function 46
3.7. Summary and discussion 49
4. Lag length selection in cointegration analysis used for SHM 52
4.1. Background 52
4.2. Conventional selection methods from econometrics 53
4.2.1. Methods based on the information criteria 53
4.2.2. Methods based on the likelihood ratio test 54
4.2.3. Methods based on the sequential modified likelihood ratio test 55
4.2.4. Sample size and lag length selection 56
4.3. Optimal lag length selection based on stationarity analysis used for structural damage detection 57
4.4. Summary and conclusions 59
5. Cointegration-based approach to SHM applications 60
5.1. Damage detection scenarios 60
5.1.1. Using geometrical features of cointegration residuals 61
5.1.2. Using wavelet variance characteristics of cointegration residuals 61
5.1.2.1. Fractal-based signal processing using wavelets 61
5.1.2.2. Wavelet-based fractal analysis of cointegration residuals 63
5.1.3. Using stationary statistical characteristics of cointegration residuals 63
5.2. Case study 1: Structural damage detection in aluminium plates using lamb waves under temperature variations 64
5.2.1. Lamb wave data contaminated by temperature 64
5.2.2. Lag length selection results 66
5.2.3. Damage detection results using cointegration residuals 67
5.2.4. Damage detection results using wavelet variance characteristics of cointegration residuals 70
5.2.5. Damage detection results using stationary statistical characteristics of cointegration residuals 76
5.3. Case study 2: Impact damage detection in composite plates using nonlinear acoustics under load changes 77
5.3.1. Principle of nonlinear vibro-acoustic wave modulation technique 77
5.3.2. Vibro-acoustic data for different frequencies of modal excitations 78
5.3.3. Lag length selection results 80
5.3.4. Damage detection results using stationary statistical characteristics of cointegration residuals 81
5.4. Summary and conclusions 84
6. Cointegration-based approach to condition monitoring of wind turbines 86
6.1. Introduction 86
6.2. Condition monitoring and fault diagnosis of wind turbines using SCADA data 87
6.2.1. Review of previous work 88
6.2.2. Discussion 90
6.3. Cointegration-based approach to condition monitoring of wind turbines 91
6.4. Experimental wind turbine data 93
6.5. Case study 1: Using various process parameters of the wind turbine 99
6.5.1. Optimal cointegrating vectors 99
6.5.2. Condition monitoring and fault detection using cointegration residuals 100
6.5.3. Discussion 104
6.6. Case study 2: Using only the temperature data of gearbox and generator 105
6.7. Summary and conclusions 109
7. Summary and conclusions 110
7.1. Summary 110
7.2. Conclusions 112
References 114