Use of Principal Component Analysis and Autoencoder Neural Networks in Characterization of Acoustic Emission Waveforms from Composite Coupon Specimens
Abstract
Acoustic emission (AE) monitoring is a method of structural health monitoring that relies on the detection of elastic waves generated by the release of concentrated strain energy when damage is created in a structural material. One of its strengths is that registered waveforms can, in theory, be used to draw conclusions about the origin of the signals, allowing to estimate damage type, location, and severity. One of the problems, however, is how to automate the interpretation and reliably retain useful information from AE waveforms, which are often thousands of samples long. In this work, a method of waveform analysis in the frequency domain is presented that combines principal compo- nent analysis and an autoencoder to reduce the dimensionality of the problem to a pair of parameters, capturing the spectrum shape and the spectrum en- ergy content. Tensile tests were carried out on composite coupon specimens while monitoring AEs, and the progression of damage was tracked by X-ray scanning them in two locations before and after tensile testing. Locations and types of damage in the scans are in good agreement with the results of the AE monitoring and analysis framework. The method is proposed as a tool for automated interpretation of AE signals with the potential to be generalized to other material, layup, and sensor setups.