DocumentCode :
1894507
Title :
Extracting Acoustical Impulse Signal of Faulty Bearing Using Blind Deconvolution Method
Author :
Wang, Yu ; Chi, Yilin ; Wu, Xing ; Liu, Chang
Author_Institution :
Fac. of Mech. & Electr. Eng., Kunming Univ. of Sci. & Technol., Kunming, China
Volume :
1
fYear :
2009
fDate :
10-11 Oct. 2009
Firstpage :
590
Lastpage :
594
Abstract :
Machine fault diagnosis, based on acoustic signals, is frequently made difficult by noisy environments at a production site. In this paper, an improved time-domain 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 time-delay 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.
Keywords :
acoustic signal processing; deconvolution; fault diagnosis; maintenance engineering; rolling bearings; statistical analysis; acoustic-based machine diagnosis; acoustical impulse signal; blind deconvolution method; cluster analysis process; defective bearings; faulty bearing; kurtosis-based criterion; machine fault diagnosis; reverberation time; rolling element bearing defect detection; time-delay; time-domain blind deconvolution algorithm; Acoustic measurements; Acoustic noise; Clustering algorithms; Computational complexity; Deconvolution; Fault diagnosis; Independent component analysis; Production; Time domain analysis; Working environment noise; acoustic signal; bearing defect detection; blind deconvolution; envelope spectrum; independent component analysis; kurtosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Conference_Location :
Changsha, Hunan
Print_ISBN :
978-0-7695-3804-4
Type :
conf
DOI :
10.1109/ICICTA.2009.149
Filename :
5287583
Link To Document :
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