Wavelet-Based Online Evaluation and Classification of Wear State Using Acoustic Emission

Advanced signal processing approaches such time-frequency analysis are widely used for online evaluation, damage detection, and wear state classification. The idea of this paper is to introduce a new methodology for online examination of wear phenomena in metallic structure by means of acoustic emission (AE), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). The proposed novel low-cost system is developed for analyzing and monitoring specific signals indicating tribological effects with focus on field programmable gate array (FPGA) implementation of discrete WT (DWT). In addition, experimental results obtained from each approach are given showing the success of the introduced approach.