Online resource; title from PDF title page (EBSCO, viewed February 21, 2018).

Includes bibliographical references and index.

Cover; Half-Title Page; Title Page; Copyright Page; Contents; Introduction; 1. Acoustic Emission: Definition and Overview; 1.1. Overview; 1.2. Acoustic waves; 1.2.1. Infinite medium: volume waves; 1.2.2. Semi-infinite medium: surface waves; 1.2.3. Guided waves; 1.2.4. Anisotropic medium and wave attenuation; 1.3. The sensors and acquisition system; 1.4. Location of sources; 1.5. The extracted descriptors from the AE signal; 1.5.1. Time domain descriptors; 1.5.2. Frequency domain descriptors; 1.5.3. Timeâ#x80;#x93;frequency analysis; 1.6. The different analyses of AE data.

1.6.1. Conventional analysis: qualitative analysis1.6.2. Multivariable statistical analysis: application of pattern recognition techniques; 1.7. Added value of quantitative acoustic emission; 2. Identification of the Acoustic Signature of Damage Mechanisms; 2.1. Selection of signals for analysis; 2.2. Acoustic signature of fiber rupture: model materials; 2.2.1. Characterization of the fiber at the scale of the bundle; 2.2.2. At the microcomposite scale; 2.2.3. At the minicomposite scale.

2.3. Discrimination using temporal descriptors of damage mechanisms in composites: single-descriptor analysis2.4. Identification of the acoustic signature of composite damage mechanisms from a frequency descriptor; 2.5. Identification of the acoustic signature of composite damage mechanisms using a time/frequency analysis; 2.6. Modal acoustic emission; 2.7. Unsupervised multivariable statistical analysis; 2.7.1. Damage identification for organic matrix composites; 2.7.2. Static fatigue damage sequence identification for a ceramic matrix composite.

2.7.3. Identification of the cyclic fatigue damage sequence for a ceramic matrix composite2.7.4. Validation of cluster labeling; 2.8. Supervised multivariable statistical analysis; 2.8.1. Library created from data based on model materials; 2.8.2. Library created from structured data by unsupervised classification; 2.9. The limits of multivariable statistical analysis based on pattern recognition techniques; 2.9.1. Performance of algorithms; 2.9.2. Influence of the acquisition conditions and the geometry of the samples; 2.10. Contribution of modeling: towards quantitative acoustic emission.

3. Lifetime Estimation3.1. Prognostic models: physical or data-oriented models; 3.2. Generalities on power laws: link with seismology; 3.3. Acoustic energy; 3.3.1. Definition of acoustic energy; 3.3.2. Taking into account coupling and definition of equivalent energy; 3.4. Identification of critical times or characteristic times in long-term tests: towards lifetime prediction; 3.4.1. The RAE emission coefficient; 3.4.2. Optimal circle contribution: highlighting the critical region; 3.4.3. The attenuation coefficient B; 3.4.4. The RLU coefficient for cyclic fatigue tests.

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.