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Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.
Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors' detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.
* Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm
* Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms
* Benefits from extensive use of the authors' detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.
Structural Health Monitoring: A Machine Learning Perspective is the first comprehensive book on the general problem of structural health monitoring. The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens and in-situ structures. This paradigm provides a comprehensive framework for developing SHM solutions.
Structural Health Monitoring: A Machine Learning Perspective makes extensive use of the authors' detailed surveys of the technical literature, the experience they have gained from teaching numerous courses on this subject, and the results of performing numerous analytical and experimental structural health monitoring studies.
* Considers structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm
* Emphasises an integrated approach to the development of structural health monitoring solutions by coupling the measurement hardware portion of the problem directly with the data interrogation algorithms
* Benefits from extensive use of the authors' detailed surveys of 800 papers in the technical literature and the experience they have gained from teaching numerous short courses on this subject.
Keith Worden, University of Sheffield, UK is Head of the Dynamics Research Group in the Department of Mechanical Engineering at the University of Sheffield. His research interests lie in the applications of advanced signal processing and machine learning methods to structural dynamics. He has authored over 400 research publications including two co-authored books on nonlinear structural dynamics and nonlinear system identification, two book chapters and over 130 refereed journal papers. He serves on the editorial boards of 2 international journals: Journal of Sound and Vibration and Mechanical Systems and Signal Processing. He was awarded "2004 Person of the Year" (jointly with W.J. Staszewski) awarded by Structural Health Monitoring journal for outstanding contribution in the field.
Preface xvii
Acknowledgements xix
1 Introduction 1
1.1 How Engineers and Scientists Study Damage 2
1.2 Motivation for Developing SHM Technology 3
1.3 Definition of Damage 4
1.4 A Statistical Pattern Recognition Paradigm for SHM 7
1.4.1 Operational Evaluation 10
1.4.2 Data Acquisition 10
1.4.3 Data Normalisation 10
1.4.4 Data Cleansing 11
1.4.5 Data Compression 11
1.4.6 Data Fusion 11
1.4.7 Feature Extraction 12
1.4.8 Statistical Modelling for Feature Discrimination 12
1.5 Local versus Global Damage Detection 13
1.6 Fundamental Axioms of Structural Health Monitoring 14
1.7 The Approach Taken in This Book 15
References 15
2 Historical Overview 17
2.1 Rotating Machinery Applications 17
2.1.1 Operational Evaluation for Rotating Machinery 18
2.1.2 Data Acquisition for Rotating Machinery 18
2.1.3 Feature Extraction for Rotating Machinery 19
2.1.4 Statistical Modelling for Damage Detection in Rotating Machinery 20
2.1.5 Concluding Comments about Condition Monitoring of Rotating Machinery 21
2.2 Offshore Oil Platforms 21
2.2.1 Operational Evaluation for Offshore Platforms 21
2.2.2 Data Acquisition for Offshore Platforms 24
2.2.3 Feature Extraction for Offshore Platforms 24
2.2.4 Statistical Modelling for Offshore Platforms 25
2.2.5 Lessons Learned from Offshore Oil Platform Structural Health Monitoring Studies 25
2.3 Aerospace Structures 25
2.3.1 Operational Evaluation for Aerospace Structures 28
2.3.2 Data Acquisition for Aerospace Structures 29
2.3.3 Feature Extraction and Statistical Modelling for Aerospace Structures 31
2.3.4 Statistical Models Used for Aerospace SHM Applications 32
2.3.5 Concluding Comments about Aerospace SHM Applications 32
2.4 Civil Engineering Infrastructure 32
2.4.1 Operational Evaluation for Bridge Structures 34
2.4.2 Data Acquisition for Bridge Structures 34
2.4.3 Features Based on Modal Properties 35
2.4.4 Statistical Classification of Features for Civil Engineering Infrastructure 36
2.4.5 Applications to Bridge Structures 36
2.5 Summary 37
References 38
3 Operational Evaluation 45
3.1 Economic and Life-Safety Justifications for Structural Health Monitoring 45
3.2 Defining the Damage to Be Detected 46
3.3 The Operational and Environmental Conditions 47
3.4 Data Acquisition Limitations 47
3.5 Operational Evaluation Example: Bridge Monitoring 48
3.6 Operational Evaluation Example: Wind Turbines 51
3.7 Concluding Comment on Operational Evaluation 52
References 52
4 Sensing and Data Acquisition 53
4.1 Introduction 53
4.2 Sensing and Data Acquisition Strategies for SHM 53
4.2.1 Strategy I 54
4.2.2 Strategy II 54
4.3 Conceptual Challenges for Sensing and Data Acquisition Systems 55
4.4 What Types of Data Should Be Acquired? 56
4.4.1 Dynamic Input and Response Quantities 57
4.4.2 Other Damage-Sensitive Physical Quantities 59
4.4.3 Environmental Quantities 59
4.4.4 Operational Quantities 60
4.5 Current SHM Sensing Systems 60
4.5.1 Wired Systems 60
4.5.2 Wireless Systems 61
4.6 Sensor Network Paradigms 63
4.6.1 Sensor Arrays Directly Connected to Central Processing Hardware 64
4.6.2 Decentralised Processing with Hopping Connection 65
4.6.3 Decentralised Processing with Hybrid Connection 66
4.7 Future Sensing Network Paradigms 67
4.8 Defining the Sensor System Properties 68
4.8.1 Required Sensitivity and Range 70
4.8.2 Required Bandwidth and Frequency Resolution 71
4.8.3 Sensor Number and Locations 71
4.8.4 Sensor Calibration, Stability and Reliability 72
4.9 Define the Data Sampling Parameters 73
4.10 Define the Data Acquisition System 74
4.11 Active versus Passive Sensing 75
4.12 Multiscale Sensing 75
4.13 Powering the Sensing System 77
4.14 Signal Conditioning 77
4.15 Sensor and Actuator Optimisation 78
4.16 Sensor Fusion 79
4.17 Summary of Sensing and Data Acquisition Issues for Structural Health Monitoring 82
References 83
5 Case Studies 87
5.1 The I-40 Bridge 87
5.1.1 Preliminary Testing and Data Acquisition 89
5.1.2 Undamaged Ambient Vibration Tests 90
5.1.3 Forced Vibration Tests 91
5.2 The Concrete Column 92
5.2.1 Quasi-Static Loading 95
5.2.2 Dynamic Excitation 95
5.2.3 Data Acquisition 95
5.3 The 8-DOF System 98
5.3.1 Physical Parameters 100
5.3.2 Data Acquisition 100
5.4 Simulated Building Structure 100
5.4.1 Experimental Procedure and Data Acquisition 101
5.4.2 Measured Data 102
5.5 The Alamosa Canyon Bridge 104
5.5.1 Experimental Procedures and Data Acquisition 104
5.5.2 Environmental Measurements 107
5.5.3 Vibration Tests Performed to Study Variability of Modal Properties 108
5.6 The Gnat Aircraft 108
5.6.1 Simulating Damage with a Modified Inspection Panel 109
5.6.2 Simulating Damage by Panel Removal 112
References 116
6 Introduction to Probability and Statistics 119
6.1 Introduction 119
6.2 Probability: Basic Definitions 120
6.3 Random Variables and Distributions 122
6.4 Expected Values 125
6.5 The Gaussian Distribution (and Others) 130
6.6 Multivariate Statistics 132
6.7 The Multivariate Gaussian Distribution 133
6.8 Conditional Probability and the Bayes Theorem 134
6.9 Confidence Limits and Cumulative Distribution Functions 137
6.10 Outlier Analysis 140
6.10.1 Outliers in Univariate Data 140
6.10.2 Outliers in Multivariate Data 141
6.10.3 Calculation of Critical Values of Discordancy or Thresholds 141
6.11 Density Estimation 142
6.12 Extreme Value Statistics 148
6.12.1 Introduction 148
6.12.2 Basic Theory 148
6.12.3 Determination of Limit Distributions 151
6.13 Dimension Reduction - Principal Component Analysis 155
6.13.1 Simple Projection 156
6.13.2 Principal Component Analysis (PCA) 156
6.14 Conclusions 158
References 159
7 Damage-Sensitive Features 161
7.1 Common Waveforms and Spectral Functions Used in the Feature Extraction Process 163
7.1.1 Waveform Comparisons 164
7.1.2 Autocorrelation and Cross-Correlation Functions 165
7.1.3 The Power Spectral and Cross-Spectral Density Functions 166
7.1.4 The Impulse Response Function and the Frequency Response Function 168
7.1.5 The Coherence Function 169
7.1.6 Some Remarks Regarding Waveforms and Spectra 170
7.2 Basic Signal Statistics 171
7.3 Transient Signals: Temporal Moments 178
7.4 Transient Signals: Decay Measures 181
7.5 Acoustic Emission Features 183
7.6 Features Used with Guided-Wave Approaches to SHM 185
7.6.1 Preprocessing 186
7.6.2 Baseline Comparisons 186
7.6.3 Damage Localisation 188
7.7 Features Used with Impedance Measurements 188
7.8 Basic Modal Properties 191
7.8.1 Resonance Frequencies 192
7.8.2 Inverse versus Forward Modelling Approaches to Feature Extraction 194
7.8.3 Resonance Frequencies: The Forward Approach 195
7.8.4 Resonance Frequencies: Sensitivity Issues 195
7.8.5 Mode Shapes 197
7.8.6 Load-Dependent Ritz Vectors 203
7.9 Features Derived from Basic Modal Properties 206
7.9.1 Mode Shape Curvature 207
7.9.2 Modal Strain Energy 210
7.9.3 Modal Flexibility 215
7.10 Model Updating Approaches 218
7.10.1 Objective Functions and Constraints 220
7.10.2 Direct Solution for the Modal Force Error 221
7.10.3 Optimal Matrix Update Methods 222
7.10.4 Sensitivity-Based Update Methods 226
7.10.5 Eigenstructure Assignment Method 230
7.10.6 Hybrid Matrix Update Methods 231
7.10.7 Concluding Comment on Model Updating Approaches 231
7.11 Time Series Models 232
7.12 Feature Selection 234
7.12.1 Sensitivity Analysis 234
7.12.2 Information Content 238
7.12.3 Assessment of Robustness 239
7.12.4 Optimisation Procedures 239
7.13 Metrics 239
7.14 Concluding Comments 240
References 240
8 Features Based on Deviations from Linear Response 245
8.1 Types of Damage that Can Produce a Nonlinear System Response 245
8.2 Motivation for Exploring Nonlinear System Identification Methods for SHM 248
8.2.1 Coherence Function 250
8.2.2 Linearity and Reciprocity Checks 251
8.2.3 Harmonic Distortion 256
8.2.4 Frequency Response Function Distortions 261
8.2.5 Probability Density Function 264
8.2.6 Correlation Tests 266
8.2.7 The Holder Exponent 266
8.2.8 Linear Time Series Prediction Errors 271
8.2.9 Nonlinear Time Series Models 273
8.2.10 Hilbert Transform 277
8.2.11 Nonlinear Acoustics Methods 279
8.3 Applications of Nonlinear Dynamical Systems Theory 280
8.3.1 Modelling a Cracked Beam as a Bilinear System 281
8.3.2 Chaotic Interrogation of a Damaged Beam 282
8.3.3 Local Attractor Variance 284
8.3.4 Detection of Damage Using the Local Attractor Variance 286
8.4 Nonlinear System Identification Approaches 288
8.4.1 Restoring Force Surface Model 288
8.5 Concluding Comments Regarding Feature Extraction Based on Nonlinear System Response 291
References 292
9 Machine Learning and...
| Erscheinungsjahr: | 2012 |
|---|---|
| Fachbereich: | Bau- und Umwelttechnik |
| Genre: | Importe, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: | 654 S. |
| ISBN-13: | 9781119994336 |
| ISBN-10: | 1119994330 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Farrar, Charles R
Worden, Keith |
| Hersteller: |
Wiley
John Wiley & Sons |
| Verantwortliche Person für die EU: | Wiley-VCH GmbH, Boschstr. 12, D-69469 Weinheim, product-safety@wiley.com |
| Maße: | 250 x 175 x 39 mm |
| Von/Mit: | Charles R Farrar (u. a.) |
| Erscheinungsdatum: | 26.12.2012 |
| Gewicht: | 1,292 kg |
Keith Worden, University of Sheffield, UK is Head of the Dynamics Research Group in the Department of Mechanical Engineering at the University of Sheffield. His research interests lie in the applications of advanced signal processing and machine learning methods to structural dynamics. He has authored over 400 research publications including two co-authored books on nonlinear structural dynamics and nonlinear system identification, two book chapters and over 130 refereed journal papers. He serves on the editorial boards of 2 international journals: Journal of Sound and Vibration and Mechanical Systems and Signal Processing. He was awarded "2004 Person of the Year" (jointly with W.J. Staszewski) awarded by Structural Health Monitoring journal for outstanding contribution in the field.
Preface xvii
Acknowledgements xix
1 Introduction 1
1.1 How Engineers and Scientists Study Damage 2
1.2 Motivation for Developing SHM Technology 3
1.3 Definition of Damage 4
1.4 A Statistical Pattern Recognition Paradigm for SHM 7
1.4.1 Operational Evaluation 10
1.4.2 Data Acquisition 10
1.4.3 Data Normalisation 10
1.4.4 Data Cleansing 11
1.4.5 Data Compression 11
1.4.6 Data Fusion 11
1.4.7 Feature Extraction 12
1.4.8 Statistical Modelling for Feature Discrimination 12
1.5 Local versus Global Damage Detection 13
1.6 Fundamental Axioms of Structural Health Monitoring 14
1.7 The Approach Taken in This Book 15
References 15
2 Historical Overview 17
2.1 Rotating Machinery Applications 17
2.1.1 Operational Evaluation for Rotating Machinery 18
2.1.2 Data Acquisition for Rotating Machinery 18
2.1.3 Feature Extraction for Rotating Machinery 19
2.1.4 Statistical Modelling for Damage Detection in Rotating Machinery 20
2.1.5 Concluding Comments about Condition Monitoring of Rotating Machinery 21
2.2 Offshore Oil Platforms 21
2.2.1 Operational Evaluation for Offshore Platforms 21
2.2.2 Data Acquisition for Offshore Platforms 24
2.2.3 Feature Extraction for Offshore Platforms 24
2.2.4 Statistical Modelling for Offshore Platforms 25
2.2.5 Lessons Learned from Offshore Oil Platform Structural Health Monitoring Studies 25
2.3 Aerospace Structures 25
2.3.1 Operational Evaluation for Aerospace Structures 28
2.3.2 Data Acquisition for Aerospace Structures 29
2.3.3 Feature Extraction and Statistical Modelling for Aerospace Structures 31
2.3.4 Statistical Models Used for Aerospace SHM Applications 32
2.3.5 Concluding Comments about Aerospace SHM Applications 32
2.4 Civil Engineering Infrastructure 32
2.4.1 Operational Evaluation for Bridge Structures 34
2.4.2 Data Acquisition for Bridge Structures 34
2.4.3 Features Based on Modal Properties 35
2.4.4 Statistical Classification of Features for Civil Engineering Infrastructure 36
2.4.5 Applications to Bridge Structures 36
2.5 Summary 37
References 38
3 Operational Evaluation 45
3.1 Economic and Life-Safety Justifications for Structural Health Monitoring 45
3.2 Defining the Damage to Be Detected 46
3.3 The Operational and Environmental Conditions 47
3.4 Data Acquisition Limitations 47
3.5 Operational Evaluation Example: Bridge Monitoring 48
3.6 Operational Evaluation Example: Wind Turbines 51
3.7 Concluding Comment on Operational Evaluation 52
References 52
4 Sensing and Data Acquisition 53
4.1 Introduction 53
4.2 Sensing and Data Acquisition Strategies for SHM 53
4.2.1 Strategy I 54
4.2.2 Strategy II 54
4.3 Conceptual Challenges for Sensing and Data Acquisition Systems 55
4.4 What Types of Data Should Be Acquired? 56
4.4.1 Dynamic Input and Response Quantities 57
4.4.2 Other Damage-Sensitive Physical Quantities 59
4.4.3 Environmental Quantities 59
4.4.4 Operational Quantities 60
4.5 Current SHM Sensing Systems 60
4.5.1 Wired Systems 60
4.5.2 Wireless Systems 61
4.6 Sensor Network Paradigms 63
4.6.1 Sensor Arrays Directly Connected to Central Processing Hardware 64
4.6.2 Decentralised Processing with Hopping Connection 65
4.6.3 Decentralised Processing with Hybrid Connection 66
4.7 Future Sensing Network Paradigms 67
4.8 Defining the Sensor System Properties 68
4.8.1 Required Sensitivity and Range 70
4.8.2 Required Bandwidth and Frequency Resolution 71
4.8.3 Sensor Number and Locations 71
4.8.4 Sensor Calibration, Stability and Reliability 72
4.9 Define the Data Sampling Parameters 73
4.10 Define the Data Acquisition System 74
4.11 Active versus Passive Sensing 75
4.12 Multiscale Sensing 75
4.13 Powering the Sensing System 77
4.14 Signal Conditioning 77
4.15 Sensor and Actuator Optimisation 78
4.16 Sensor Fusion 79
4.17 Summary of Sensing and Data Acquisition Issues for Structural Health Monitoring 82
References 83
5 Case Studies 87
5.1 The I-40 Bridge 87
5.1.1 Preliminary Testing and Data Acquisition 89
5.1.2 Undamaged Ambient Vibration Tests 90
5.1.3 Forced Vibration Tests 91
5.2 The Concrete Column 92
5.2.1 Quasi-Static Loading 95
5.2.2 Dynamic Excitation 95
5.2.3 Data Acquisition 95
5.3 The 8-DOF System 98
5.3.1 Physical Parameters 100
5.3.2 Data Acquisition 100
5.4 Simulated Building Structure 100
5.4.1 Experimental Procedure and Data Acquisition 101
5.4.2 Measured Data 102
5.5 The Alamosa Canyon Bridge 104
5.5.1 Experimental Procedures and Data Acquisition 104
5.5.2 Environmental Measurements 107
5.5.3 Vibration Tests Performed to Study Variability of Modal Properties 108
5.6 The Gnat Aircraft 108
5.6.1 Simulating Damage with a Modified Inspection Panel 109
5.6.2 Simulating Damage by Panel Removal 112
References 116
6 Introduction to Probability and Statistics 119
6.1 Introduction 119
6.2 Probability: Basic Definitions 120
6.3 Random Variables and Distributions 122
6.4 Expected Values 125
6.5 The Gaussian Distribution (and Others) 130
6.6 Multivariate Statistics 132
6.7 The Multivariate Gaussian Distribution 133
6.8 Conditional Probability and the Bayes Theorem 134
6.9 Confidence Limits and Cumulative Distribution Functions 137
6.10 Outlier Analysis 140
6.10.1 Outliers in Univariate Data 140
6.10.2 Outliers in Multivariate Data 141
6.10.3 Calculation of Critical Values of Discordancy or Thresholds 141
6.11 Density Estimation 142
6.12 Extreme Value Statistics 148
6.12.1 Introduction 148
6.12.2 Basic Theory 148
6.12.3 Determination of Limit Distributions 151
6.13 Dimension Reduction - Principal Component Analysis 155
6.13.1 Simple Projection 156
6.13.2 Principal Component Analysis (PCA) 156
6.14 Conclusions 158
References 159
7 Damage-Sensitive Features 161
7.1 Common Waveforms and Spectral Functions Used in the Feature Extraction Process 163
7.1.1 Waveform Comparisons 164
7.1.2 Autocorrelation and Cross-Correlation Functions 165
7.1.3 The Power Spectral and Cross-Spectral Density Functions 166
7.1.4 The Impulse Response Function and the Frequency Response Function 168
7.1.5 The Coherence Function 169
7.1.6 Some Remarks Regarding Waveforms and Spectra 170
7.2 Basic Signal Statistics 171
7.3 Transient Signals: Temporal Moments 178
7.4 Transient Signals: Decay Measures 181
7.5 Acoustic Emission Features 183
7.6 Features Used with Guided-Wave Approaches to SHM 185
7.6.1 Preprocessing 186
7.6.2 Baseline Comparisons 186
7.6.3 Damage Localisation 188
7.7 Features Used with Impedance Measurements 188
7.8 Basic Modal Properties 191
7.8.1 Resonance Frequencies 192
7.8.2 Inverse versus Forward Modelling Approaches to Feature Extraction 194
7.8.3 Resonance Frequencies: The Forward Approach 195
7.8.4 Resonance Frequencies: Sensitivity Issues 195
7.8.5 Mode Shapes 197
7.8.6 Load-Dependent Ritz Vectors 203
7.9 Features Derived from Basic Modal Properties 206
7.9.1 Mode Shape Curvature 207
7.9.2 Modal Strain Energy 210
7.9.3 Modal Flexibility 215
7.10 Model Updating Approaches 218
7.10.1 Objective Functions and Constraints 220
7.10.2 Direct Solution for the Modal Force Error 221
7.10.3 Optimal Matrix Update Methods 222
7.10.4 Sensitivity-Based Update Methods 226
7.10.5 Eigenstructure Assignment Method 230
7.10.6 Hybrid Matrix Update Methods 231
7.10.7 Concluding Comment on Model Updating Approaches 231
7.11 Time Series Models 232
7.12 Feature Selection 234
7.12.1 Sensitivity Analysis 234
7.12.2 Information Content 238
7.12.3 Assessment of Robustness 239
7.12.4 Optimisation Procedures 239
7.13 Metrics 239
7.14 Concluding Comments 240
References 240
8 Features Based on Deviations from Linear Response 245
8.1 Types of Damage that Can Produce a Nonlinear System Response 245
8.2 Motivation for Exploring Nonlinear System Identification Methods for SHM 248
8.2.1 Coherence Function 250
8.2.2 Linearity and Reciprocity Checks 251
8.2.3 Harmonic Distortion 256
8.2.4 Frequency Response Function Distortions 261
8.2.5 Probability Density Function 264
8.2.6 Correlation Tests 266
8.2.7 The Holder Exponent 266
8.2.8 Linear Time Series Prediction Errors 271
8.2.9 Nonlinear Time Series Models 273
8.2.10 Hilbert Transform 277
8.2.11 Nonlinear Acoustics Methods 279
8.3 Applications of Nonlinear Dynamical Systems Theory 280
8.3.1 Modelling a Cracked Beam as a Bilinear System 281
8.3.2 Chaotic Interrogation of a Damaged Beam 282
8.3.3 Local Attractor Variance 284
8.3.4 Detection of Damage Using the Local Attractor Variance 286
8.4 Nonlinear System Identification Approaches 288
8.4.1 Restoring Force Surface Model 288
8.5 Concluding Comments Regarding Feature Extraction Based on Nonlinear System Response 291
References 292
9 Machine Learning and...
| Erscheinungsjahr: | 2012 |
|---|---|
| Fachbereich: | Bau- und Umwelttechnik |
| Genre: | Importe, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: | 654 S. |
| ISBN-13: | 9781119994336 |
| ISBN-10: | 1119994330 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Farrar, Charles R
Worden, Keith |
| Hersteller: |
Wiley
John Wiley & Sons |
| Verantwortliche Person für die EU: | Wiley-VCH GmbH, Boschstr. 12, D-69469 Weinheim, product-safety@wiley.com |
| Maße: | 250 x 175 x 39 mm |
| Von/Mit: | Charles R Farrar (u. a.) |
| Erscheinungsdatum: | 26.12.2012 |
| Gewicht: | 1,292 kg |
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