Evaluation of the use of envelope analysis and DWT on AE signals generated from degrading shafts

Abstract

Abstract Vibration analysis is widely used in machinery diagnosis. Wavelet transforms and envelope analysis, which have been implemented in many applications in the condition monitoring of machinery, are applied in the development of a condition monitoring system for early detection of faults generated in several key components of machinery. Early fault detection is a very important factor in condition monitoring and a basic component for the application of condition-based maintenance (CBM) and predictive maintenance (PM). In addition, acoustic emission (AE) sensors have specific characteristics that are highly sensitive to high-frequency and low-energy signals. Therefore, the AE technique has been applied recently in studies on the early detection of failure. In this paper, AE signals caused by crack growth on a rotating shaft were captured through an AE sensor. The AE signatures were pre-processed using the proposed signal processing method, after which power spectrums were generated from the FFT results. In the power spectrum, some peaks from fault frequencies were presented. According to the results, crack growth in rotating machinery can be considered and detected using an AE sensor and the signal processing method.