Structural Health Monitoring, Damage Detection & Mechatronics, Volume 7 (eBook)

Dr. Alfred L Wicks is Associate Professor of Mechanical Engineering at Vurginia Tech; Dr. Christopher Niezrecki is a Professor and Director of the Center for Wind Energy, Department of Mechanical Engineering UMass Lowell.

Preface 6
Contents 8
Chapter 1: Development and Characterization of an ITO Nanocomposite Film Sensor for Damage Detection 10
1.1 Introduction 10
1.2 Surface Damage Detecting Film Nanocomposite 11
1.3 Experimental Setup 11
1.4 Results and Discussions 12
1.4.1 Surface Damage Detection 12
1.4.2 Influence of the Temperature 14
1.4.3 Influence of the Humidity 14
1.5 Conclusion 14
References 15
Chapter 2: Fiber Optic Sensor Arrays for Real-Time Virtual Instrumentation and Control of Flexible Structures 17
2.1 Introduction 17
2.2 Approach 18
2.3 Finite Element Analysis 18
2.4 Model Formulation 21
2.5 FBG-IMU System Development (First Approach) 21
2.6 Second Approach of FBG-IMU 24
2.7 Results 28
2.8 Conclusions and Future Work 30
References 30
Chapter 3: On the Output-Only Vibration-Based Damage Detection of Frame Structures 31
3.1 Introduction 31
3.2 Output-Only Vibration-Based Damage Detection Methods 32
3.3 Application: Identification and Damage Detection 32
3.3.1 Test Structure: Undamaged Condition 33
3.3.2 Test Structure: Damaged Condition 34
3.3.3 Simulation of the Structural Response 34
3.4 Results and Discussion 35
3.4.1 Output-Only Modal Identification 35
3.4.2 Damage Detection 37
3.5 Conclusions 40
References 40
Chapter 4: On the Influence of Sample Length and Measurement Noise on the Stochastic Subspace Damage Detection Technique 42
4.1 Introduction 42
4.2 Stochastic Subspace Damage Detection Technique 43
4.2.1 Dynamic Equilibrium Equation in Discrete Time Domain 43
4.2.2 Output-Only Covariance Based Subspace System Identification 44
4.2.3 Residual Vector Formation 45
4.2.4 Hypothesis Test 46
4.2.4.1 Parametric Chi-Square Test 46
4.2.4.2 Non-parametric Chi-Square Test 46
4.3 Investigating the Effect of Noise and Number of Samples 47
4.3.1 Effect of Number of Samples 47
4.3.1.1 Effect on the Residual Covariance 47
4.3.1.2 Effect on the chi2 Test Value 47
4.3.2 Effect of Measurement Noise 48
4.3.2.1 Equal Noise Properties Between the Reference State and Possibly Damaged State 49
4.3.2.2 Different Noise Properties Between the Reference State and Possibly Damaged State 49
4.4 Numerical Application 50
4.4.1 Cases Study 1, Effect of Number of Samples 50
4.4.2 Case Study 2, Effect of Noise with Equal Properties 50
4.4.3 Case Study 3, Effect of Noise with Unequal Properties 51
4.5 Discussion and Conclusion 52
References 53
Chapter 5: Quantification of Structural Damage with Self-Organizing Maps 54
5.1 Introduction 54
5.2 Qatar University Grandstand Simulator 55
5.3 Verification of the Finite Element Model 56
5.4 The SOM-Based Damage Detection Algorithm 56
5.4.1 Training of the SOMs 57
5.4.2 Structural Damage Assessment 59
5.5 Numerical Demonstration of the Damage Detection Algorithm 60
5.6 Discussion 61
5.7 Conclusion 63
References 64
Chapter 6: Accuracy Enhancement of a Device for Automated Underbridge Inspections 65
6.1 Introduction 65
6.2 Method 66
6.3 Results 69
6.4 Concluding Remarks 71
References 71
Chapter 7: A Brief Overview of Mechatronics 73
7.1 Introduction 73
7.2 Background 73
7.3 Basic Microcontroller 75
7.4 Actuation 76
7.5 Sensors 76
7.6 Modeling/Simulation 78
7.7 Bus Structures 79
7.8 Batteries 80
7.9 Conclusions 80
References 80
Chapter 8: Enhanced Vibration Damping by Means of a Negative Capacitance 81
8.1 Introduction 81
8.2 Analytical Model of the Electro-Mechanical System 82
8.3 Structure of the Electric Impedance 83
8.4 Stability of the Electro-Mechanical System 86
8.5 Conclusion 87
References 87
Chapter 9: Vehicle Tracking for Bridge Load Dynamics Using Vision Techniques 88
9.1 Introduction 88
9.2 Vehicle Tracking Algorithm 88
9.2.1 Types of Vehicle Detection Schemes 88
9.2.2 Overview of Vehicle Location Algorithm 89
9.2.3 Bridge Projective Transformation 89
9.2.4 Background Subtraction and Vehicle Tracking 90
9.3 Experiments 90
9.3.1 Simulated Data Creation 91
9.3.2 Experiments on Simulated Data 91
9.4 Results of Experiments 91
9.4.1 Accuracy of Location 91
9.4.2 Types of Error and Potential Causes 92
9.5 Conclusions and Future Work 93
References 94
Chapter 10: Model Based System Testing: Bringing Testing and Simulation Close Together 96
10.1 Introduction 96
10.2 Testing for Simulation: Simulation of Switched Reluctance Motors 98
10.3 Simulation for Testing: Electric Power Steering 99
10.4 Testing with Simulation: Model-Based High Fidelity Steering Wheel Force Feedback 100
10.5 Conclusions 101
References 102
Chapter 11: Time-Varying System Identification Using a Hybrid Blind Source Separation Method 103
11.1 Introduction 103
11.2 Hybrid BSS Method 104
11.3 Experimental Study 105
11.4 Conclusions 107
References 107
Chapter 12: An Energy Measure for Mode Localization 109
12.1 Introduction 109
12.2 The Measure of Mode Localization 110
12.3 A Simple Mass-Spring Example 110
12.4 An Idealized Bladed Disk Example 112
12.5 Conclusions 113
References 114
Chapter 13: Vibration Control on a Space Flexible Structure with a PZT Stack Actuator Using Strain and MPPF Control 115
13.1 Introduction 115
13.2 Modeling of Flexible Frame Structure with a PZT Stack Actuator 116
13.3 Vibration Control Using MPPF and Direct Strain Feedback 119
13.3.1 Application of Multiple Positive Position Feedback (MPPF) 119
13.4 Experimental Vibration Control Using MPPF and MPPF+DSF 120
13.4.1 Some Experimental Results 121
13.5 Conclusions 122
References 122
Chapter 14: Multi-Reference Time-Frequency Active Control of Vehicle Interior Road Noise 124
14.1 Introductions 124
14.2 Control System 125
14.2.1 FXLMS Algorithm 125
14.2.2 Time-Frequency FXLMS Algorithm 126
14.2.3 SFXLMS Algorithm 127
14.3 Computational Complexity Analysis 128
14.4 Numerical Results 129
14.5 Conclusions 130
References 130
Chapter 15: Wireless Monitoring of the Dynamic Behavior of Railway Catenary Systems 132
15.1 Introduction 132
15.2 Railway Catenary Systems 133
15.3 Wireless Sensors and Data Acquisition System for Electric Railway Catenaries 134
15.4 Operational Modal Analysis for Dynamic Assessment of the Railway Catenary System 135
15.4.1 Power Spectral Density (PSD) Estimation 135
15.4.2 The Short-Time Fourier Transform (STFT) and Spectrogram Analysis 136
15.4.3 Covariance Driven Stochastic Subspace Identification (Cov-SSI) Method 136
15.5 Full-Scale Measurements of Soft Norwegian Catenaries for the Electric Railways 136
15.5.1 Hovin Section, Description and Instrumentation 136
15.6 Results and Discussion 137
15.7 Conclusions 141
References 141
Chapter 16: Functional Series TARMA Models for Non-stationary Tool Vibration Signals Representation and Wear Estimation 143
16.1 Introduction 143
16.2 Functional Series TARMA Models and Their Estimation 144
16.2.1 FS-TARMA Models 145
16.2.2 FS-TARMA Parameter Estimation 146
16.2.3 Fault Detection and Identification by Model Parameter-Based Method 146
16.2.3.1 Fault Detection 146
16.2.3.2 Fault Identification and Estimation 147
16.3 Major Flank Wear Estimation 147
16.3.1 Estimation of FS-TARMA Models 149
16.3.2 Wear Estimation via Model Parameter-Based Method 149
16.4 Conclusion 152
References 153