In this book, two kinds of analysis based on acoustic emission recorded during mechanical tests are investigated.
In the first, individual, analysis, acoustic signature of each damage mechanism is characterized. So with a clustering method, AE signals that have similar shapes or similar features can be group together into a cluster. Afterwards, each cluster can be linked with a main damage. The second analysis is based on a global AE analysis, on the investigation of liberated energy, with a view to identify a critical point. So beyond this characteristic point, the criticality can be modeled with a power-law in order to evaluate time to failure.
Table of Contents
Introduction ix
Chapter 1. Acoustic Emission: Definition and Overview 1
1.1. Overview 1
1.2. Acoustic waves 8
1.2.1. Infinite medium: volume waves 8
1.2.2. Semi-infinite medium: surface waves 9
1.2.3. Guided waves 9
1.2.4. Anisotropic medium and wave attenuation 10
1.3. The sensors and acquisition system 12
1.4. Location of sources 16
1.5. The extracted descriptors from the AE signal 21
1.5.1. Time domain descriptors 22
1.5.2. Frequency domain descriptors 26
1.5.3. Time–frequency analysis 30
1.6. The different analyses of AE data 32
1.6.1. Conventional analysis: qualitative analysis 32
1.6.2. Multivariable statistical analysis: application of pattern recognition techniques 42
1.7. Added value of quantitative acoustic emission 55
Chapter 2. Identification of the Acoustic Signature of Damage Mechanisms 59
2.1. Selection of signals for analysis 59
2.2. Acoustic signature of fiber rupture: model materials 63
2.2.1. Characterization of the fiber at the scale of the bundle 64
2.2.2. At the microcomposite scale 69
2.2.3. At the minicomposite scale 72
2.3. Discrimination using temporal descriptors of damage mechanisms in composites: single-descriptor analysis 75
2.4. Identification of the acoustic signature of composite damage mechanisms from a frequency descriptor 79
2.5. Identification of the acoustic signature of composite damage mechanisms using a time/frequency analysis 81
2.6. Modal acoustic emission 82
2.7. Unsupervised multivariable statistical analysis 84
2.7.1. Damage identification for organic matrix composites 85
2.7.2. Static fatigue damage sequence identification for a ceramic matrix composite 89
2.7.3. Identification of the cyclic fatigue damage sequence for a ceramic matrix composite 92
2.7.4. Validation of cluster labeling 96
2.8. Supervised multivariable statistical analysis 100
2.8.1. Library created from data based on model materials 100
2.8.2. Library created from structured data by unsupervised classification 103
2.9. The limits of multivariable statistical analysis based on pattern recognition techniques 104
2.9.1. Performance of algorithms 105
2.9.2. Influence of the acquisition conditions and the geometry of the samples 113
2.10. Contribution of modeling: towards quantitative acoustic emission 120
Chapter 3. Lifetime Estimation 123
3.1. Prognostic models: physical or data-oriented models 125
3.2. Generalities on power laws: link with seismology 128
3.3. Acoustic energy 133
3.3.1. Definition of acoustic energy 133
3.3.2. Taking into account coupling and definition of equivalent energy 134
3.4. Identification of critical times or characteristic times in long-term tests: towards lifetime prediction 136
3.4.1. The RAE emission coefficient 137
3.4.2. Optimal circle contribution: highlighting the critical region 139
3.4.3. The attenuation coefficient B 140
3.4.4. The RLU coefficient for cyclic fatigue tests 142
3.4.5. The coupling between acoustic energy and mechanical energy: the Sentry function 144
3.5. Simulation of the release of energy using a power law: prediction of the rupture time 146
Conclusion 151
Bibliography 153
Index 181
