Structural Health Monitoring and Damage Detection, Volume 7

Preface	6
Contents	8
1 Bearing Faults Simulations Through a Parametric Model of a Gearbox	10
1.1 Introduction	10
1.2 Kinetostatic Model of the Transmission	11
1.3 Models of Defects on Bearings	13
1.4 Experimental Frequency Response Function	14
1.5 Test Case	16
1.6 Concluding Remarks	18
References	18
2 Sensitivity Evaluation of Subspace-Based Damage Detection Method to Different Types of Damage	19
2.1 Introduction	19
2.2 Statistical Subspace-Based Damage Detection	20
2.2.1 Models and Parameters	20
2.3 Damage and Data Simulation	21
2.4 Case Study	22
2.4.1 Damage Simulation	22
2.4.2 Data Simulation	23
2.4.3 Damage Detection	23
2.5 Conclusion	24
References	26
3 An Improved Blind Source Separation for Structural Mode Identification Using Fewer Measurements	27
3.1 Introduction	27
3.2 Formulation of the Algorithm	28
3.2.1 Efficient Underdetermined Source Separation	29
3.2.1.1 Lag Zero PARAFAC Decomposition	29
3.2.1.2 Finite-Lag PARAFAC Decomposition	30
3.3 Experimental Results	30
3.4 Conclusions	32
References	33
4 Real Time NDE of Cold Spray Processing Using Acoustic Emission	34
4.1 Introduction	34
4.2 Experimental Approach	35
4.3 Experimental Results and Discussion	36
4.3.1 Analysis of a 90Ta-10W on Steel with Parameters Known to Result in Poor Bonding	37
4.3.2 Analysis of a 90Ta-10W on Aluminum with Parameters Known to Resultin Good Bonding	37
4.3.3 Analysis of a 90Ta-10W on Aluminum with Powder Which ContainedLarge Agglomerates	38
4.3.4 Analysis of a 90Ta-10W on Aluminum with Powder Which Contained Large Agglomerates and Delaminated	40
4.4 Discussion	41
4.5 Conclusion	42
References	43
5 Prototyping and Testing of a Graphene-Oxide Tamper Evident Seal	44
5.1 Introduction	44
5.2 Theoretical Background	45
5.3 Prototype Description	46
5.4 Model Results	48
5.5 Testing Results	50
5.6 Summary/Conclusion	50
References	51
6 Solitary Waves to Infer Axial Stress in Slender Structures: A Numerical Model	53
6.1 Introduction	53
6.2 One-Chain Configuration	54
6.2.1 Numerical Formulation	54
6.2.2 Numerical Setup	55
6.2.3 Numerical Results	55
6.3 Two-Chain Configuration	57
6.3.1 Numerical Formulation	58
6.3.2 Numerical Setup	58
6.3.3 Numerical Results	58
6.4 Effect of the Granules' Properties	60
6.5 Conclusions	62
References	63
7 Are Today's SHM Procedures Suitable for Tomorrow's BIGDATA?	64
7.1 Introduction	64
7.2 Exactly How Big Is “BIGDATA”?	64
7.3 Computational Sensitivity of SHM Procedures	65
7.4 Preprocessing Application	65
7.5 System Identification Application	67
7.6 Damage Detection Application	67
7.7 Conclusions	69
References	69
8 Static Deformation Analysis for Structural Health Monitoring of a Large Dam	71
8.1 Introduction	71
8.2 Static Deformation Measurement of Fei-Tsui Dam	72
8.3 Modeling Technique	73
8.4 Results of Static Deformation Monitoring of Fei-Tsui Dam	74
8.5 Conclusions	79
References	85
9 Operational Vibration-Based Response Estimation for Offshore Wind Lattice Structures	87
9.1 Introduction	87
9.2 Method	88
9.2.1 Joint Input-State Estimation	88
9.2.2 Response Estimation	89
9.2.3 Wind Turbine Model	90
9.2.4 Stochastic Wind Force	90
9.2.5 Stochastic Wave Force	92
9.2.6 Sensor Network	93
9.3 Results	95
9.3.1 Response Estimation in the Absence of Modelling Errors	95
9.3.2 Response Estimation with an Erroneous Model	97
9.4 Discussion and Conclusions	98
References	100
10 Autoregressive Model Applied to the Meazza Stadium for Damage Detection	101
10.1 Introduction	101
10.2 Theory and Description of the Adopted Models	102
10.2.1 Autoregressive Models: AR	102
10.2.2 Optimal Order Estimation	102
10.2.2.1 Partial Autocorrelation Function (PACF)	103
10.2.2.2 Akaike's Information Criterion (AIC)	103
10.2.2.3 Bayesian Information Criterion (BIC)	103
10.2.3 Mahalanobis Distance	104
10.3 G. Meazza Statium	104
10.3.1 Dynamic Behaviour of the Stand	106
10.4 Data Analysis	107
10.4.1 Basic Statistic	109
10.4.2 Autoregressive Model	110
10.4.2.1 Model Order Estimation	111
10.4.2.2 Mahalanobis Distance on Parameters	111
10.5 Conclusions	112
References	113
11 Output Only Functional Series Time Dependent AutoRegressive Moving Average (FS-TARMA) Modelling of Tool Acceleration Signals for Wear Estimation	114
11.1 Introduction	114
11.2 Functional Series TARMA Models of Signals and Their Estimation	116
11.2.1 FS-TARMA Models	116
11.2.2 FS-TARMA Parameter Estimation	117
11.2.3 Distance Between the FS-TARMA Models	117
11.3 Major Flank Wear Estimation	118
11.3.1 Description of Experiments	118
11.3.2 Tool Wear Measurement	120
11.3.3 FS-TARMA Estimation	120
11.3.4 Analysis Based on the Martin's Distance	120
11.4 Conclusion	123
References	124
12 A High-Speed Dual-Stage Ultrasonic Guided Wave System for Localizationand Characterization of Defects	126
12.1 Introduction	126
12.2 Background	127
12.3 Experimental Procedures	128
12.3.1 Equipment and Preparation Considerations	128
12.3.1.1 Transducer Attachment	128
12.3.1.2 Frequency Selection	128
12.3.2 Baseline Acquisition	128
12.3.3 Sensor Auto-Localization	129
12.3.4 Stage 1 – Detection and Rough Localization Using Rayleigh Maximum Likelihood Estimation	130
12.3.4.1 “Ping” Mode	130
12.3.4.2 Optimal Baseline Subtraction and Damage Detection	130
12.3.4.3 Rayleigh Maximum Likelihood Estimate	131
12.3.5 Stage 2 – High Fidelity Characterization Using Acoustic Wavenumber Spectroscopy	133
12.3.5.1 Scan Area Selection	134
12.3.5.2 Active Transducer Selection	134
12.3.5.3 Laser Scan	135
12.4 Experiments	135
12.4.1 Laboratory Experiments	135
12.4.1.1 Test Setup	136
12.4.1.2 Results and Discussion	136
12.4.2 Field Experiments	137
12.4.2.1 Test Setup	137
12.4.2.2 Results and Discussion	137
12.5 Conclusion	138
References	139
13 Vibration-Based Scour Monitoring: Prototype Design, Laboratory Experiments and Field Deployment	140
13.1 Introduction	140
13.2 Sensor Development: General Concepts	141
13.3 Laboratory Testing	142
13.4 Field Deployment: Preliminary Results	143
13.5 Conclusions	145
References	147
14 Monitoring Fatigue Life Expenditure & Detecting Crack Initiation	148
14.1 Introduction	148
14.2 Airframe Structural Failure Mechanisms	149
14.3 Analytical Model	150
14.4 Failure Detection via Frequency Response	151
14.5 New Approach	151
14.6 Test Methodology	151
14.7 Honeycomb Laminate Control Surface	153
14.8 Conclusions	156
14.9 Opportunities for Further Investigation	156
14.10 Experimental Issues	157
References	158
15 Characterization and Prognosis of Multirotor Failures	159
15.1 Introduction	159
15.1.1 Motivation	159
15.1.2 Background	160
15.1.3 Project Scope	160
15.2 Instrumentation and Experimentation	161
15.2.1 Testing Platforms	161
15.3 Measureable Failure Modes	161
15.3.1 Motor	162
15.3.2 Propeller	162
15.3.3 Electronic Speed Controller (ESC)	163
15.3.4 Battery	163
15.4 Testing Procedure	164
15.4.1 Single Rotor Testing Procedure	164
15.4.2 Multirotor Testing Procedure	165
15.4.3 Data Processing	166
15.5 Results	166
15.5.1 Lift/Load Cell	167
15.5.1.1 Single Rotor	167
15.5.2 Temperature/Thermocouple	167
15.5.3 Current or Power/Hall Effect Sensor	168
15.5.3.1 Single Rotor	168
15.5.3.2 Quad-Rotor	169
15.5.4 Vibrations/Accelerometer	170
15.5.4.1 Magnitude of Vibrations	170
15.5.4.2 Vibrations in the Frequency Domain	171
15.5.5 Additional Observations	173
15.5.5.1 Battery	173
15.5.5.2 ESC	173
15.6 Conclusion	174
References	175
16 Statistical Tools for the Characterization of Environmental and Operational Factors in Vibration-Based SHM	176
16.1 Introduction	176
16.2 Removal of the Influence of Environmental Factors from NaturalFrequency Estimates	177
16.3 Second Order Blind Identification for Removal of Environmental Effects	179
16.4 Proof of Concept	181
16.5 Conclusions	185
References	185
17 An Experimental Investigation of Feature Availability in Nominally Identical Structures for Population-Based SHM	186
17.1 Introduction	186
17.2 Test Structures and Data Acquisition	187
17.3 Feature Comparison of the Structures	188
17.3.1 Natural Frequencies and Mode Shapes	188
17.3.2 FRF Features	189
17.4 Conclusions	191
References	192

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