No Detail Found!

Fault feature extracting for rotating machinery vibration based on blind deconvolution and spectral kurtosis.

Author(s)
Weiguo Huang School of Urban Rail Transportation, Soochow University, Suzhou 215021, China
Haiyang Liu School of Urban Rail Transportation, Soochow University, Suzhou 215021, China
Zhongkui Zhu School of Urban Rail Transportation, Soochow University, Suzhou 215021, China
Zhiyong He School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China
Abstract
Rotating machinery vibration analysis involves a convolute mixture because of the propagation medium, and the signals recorded by sensors in an industrial application are often disrupted by the environment. Deconvolution is a signal processing method for convolution of vibration sources, spectral kurtosis is a statistical tool which can indicate the presence of series of transients and their locations in the frequency domain in strong noise case. In this paper, we propose an approach for the two characteristics based on blind deconvolution and spectral kurtosis. First the two methods blind deconvolution and spectral kurtosis are reviewed, and then puts forward the combination of the two methods to extract the fault feature from multi-sources convolution and strong noise in rotating machinery vibration, Finally apply the combination method to a bearing failure test, the test results show good performance for extraction of fault features in rotating machinery vibration.
Publication Details
Page(s) 261-266
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 43, Issue: 2, Year: 2012

Request the full text from PASTIC

More from Journal of Theoretical and Applied Information Technology
0

Citations

0

Downloads

113

Views

Copyright ©  PASTIC National Center, Islamabad www.pastic.gov.pk