Signal Processing and Pattern Recognition of Ae Signatures

Abstract

Signal processing and pattern recognition are extensively used in AE data evaluation in order to discriminate relevant from non relevant indications, characterize the source of emission and correlate with the associated failure mechanism. Signal processing is performed at the waveform level, either by applying digital filtering, Fourier transforms or other processing such as wavelet transform, or by extracting AE features as a mean to describe the shape and content of a detected AE waveform. In either case the aim is to discriminate one type of waveform from another and correlate with different source mechanisms. The use of histogram analysis and/or two dimensional correlation plots is discussed as conventional AE signature identification process. The paper shows that the algorithms and respective software offers all the necessary tools for evaluating the complexity of the problem and proceed with classifier design for AE signatures recognition.