Acoustic Emission Signal Processing Research Based On HHT

Acoustic emission signal processing methods are an important part of AE inspection techniques and are also the main basis to make assessment for detected objects. Acoustic emission signals with transient and random, which are made up of a series of signals rich in frequency and pattern, belong to non-stationary random signals. Traditional Fourier transformation is a total transformation, and owing to using time-varied function as statistics it has significant limitation on analyzing non-stationary signals. Therefore application of Hilbert-huang transformation has practical significance for acoustic emission signal processing.In this paper, based on Hilbert-huang transformation theory, acoustic emission signals and actual signals were respectively analyzed by using Hilbert-huang transformation, and under laboratory condition acoustic emission signal type in compression fracture process of concrete beams was studied and its results after Hilbert-huang transformation were showed as well. Finally, combining Hilbert-huang transformation and independent component analyzed acoustic emission signals for characteristic extraction. The result in acoustic emission signal analysis provides experimental basis for Hilbert-huang transformation. The content in this paper as follows:1. Whether Hilbert-huang transformation could be applied to acoustic emission signal processing was carried out theoretical analysis, and then analogue signals were used to verify feasibility that Hilbert-huang transformation can be applied to acoustic emission signal processing.2. To compare Hilbert-huang transformation and other traditional time-frequency analysis methods which were applied to characteristic extraction of analogue signals and actual signals.3. To monitor acoustic emission signals in compression fracture process of concrete beams, and through parametric analysis, spectral analysis, and Hilbert-huang transformation characteristics of acoustic emission signals were showed in different stages of compression damage.4. To combine independent component analysis with Hilbert-huang transformation, and it was effective to avoid the 'too decomposed' situation in EMD decomposition process by utilizing Blind Source Separation characteristic of Independent Component Analysis.