Noise suppression in acoustic emission data was attempted by developing and using artificial neural networks (ANN) and with the long-term objective of in-flight monitoring. In-flight experiments conducted earlier and the noise characteristics outlined therein were taken as basis for their simulation in the laboratory. Simulated noise sources were classified through both supervised and a combination of un-supervised and supervised training of ANN. AE signals were generated by fatigue spectrum load tests on CFRP specimens and their failure modes were characterized. Finally, simulated noise and the actual signals were mixed and re-classified into their respective classes. The results obtained are encouraging and the methods and procedures adopted confirm the feasibility of the approach for field applications.
All Science Journal Classification (ASJC) codes
- Ceramics and Composites
- Civil and Structural Engineering