Extracting Acoustical Impulse Signal of Faulty Bearing Using Blind Deconvolution Method
Machine fault diagnosis, based on acoustic signals, is frequently made difficult by noisy environments at a production site. In this paper, an improved timedomain blind deconvolution algorithm, based on envelope spectrum and normalized kurtosis, was proposed to recover acoustic signals of defective bearings. A newly defined distance measure based on envelope spectrum was employed to improve the classification accuracy of independent components in the cluster analysis process, and a kurtosis-based criterion was applied to select optimum components. With the help of these enhancements, reliable estimated results can be obtained with low computational complexity, even when the timedelay or the reverberation time is sufficiently large. Both numerical and experimental studies were carried out. The results show that this algorithm can be efficiently applied to rolling element bearing defect detection in real-world situations, and is very promising in acoustic-based machine diagnosis.
bearing defect detection acoustic signal blind deconvolution independent component analysis envelope spectrum kurtosis
Yu Wang Yilin Chi Xing Wu Chang Liu
Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming, China
国际会议
长沙
英文
590-594
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）