Feature Estimation of AE Signals caused by Rotating Shaft with Crack Growth
Vibration analysis is widely used in machinery diagnosis, and the wavelet transforms and envelope analysis had also been implemented in many applications in the condition monitoring of machinery. Wavelet transform is using in researches for detection of the faults in gearboxes. These are applied for a development of the condition monitoring system for early detection of the faults generated in several key components of machinery. Early detection of the faults is a very important factor for condition monitoring and a basic component to extend CBM (Condition-Based Maintenance) to PM (Prediction Maintenance). And AE (acoustic emission) sensor has a specific characteristic on the high sensitivity of the signal, high frequency and low energy, so recently AE technique is applied in some research papers for the early detection of machine fault. In this paper, the AE signals caused by the crack growth on the rotating shaft were acquired through AE sensor. The AE signals were preprocessed by wavelet transforms, and then the power spectrums of the frequency domain were generated using the FFT result of those. In the power spectrum, some peaks were presented on the several frequencies, and the characteristics considered on the crack growth were found out.
Dongsik Gu Jae Gu Kim Young Chan Kim Won Jo Park Byeong Keun Choi
Department of Precision and Mechanical Engineering, Gyeongsang National University Graduate School, Doosan Heavy Industries, 555 Guigok-dong, Changwon City, Gyeongnam-do, 641-792, South Korea Department of Energy and Mechanical Engineering, Institute of Marine Industry, Gyeongsang National U
国际会议
桂林
英文
1-8
2010-11-16（万方平台首次上网日期，不代表论文的发表时间）