Application of Frequency-Domain Blind Deconvolution in Mechanical Fault Detection

Author:

Pan Nan1,Xing Wu1,Chi Yi Lin1,Chang Liu1,Liu Xiao Qin1

Affiliation:

1. Kunming University of Science and Technology

Abstract

On the basis of summing up the Frequency-Domain Blind Deconvolution (FDBD), a method combine Complex-Domain FastICA algorithm and amplitude correlation was proposed to extract the typical defect signals from mechanical equipment. The application in combined failure rolling bearing acceleration signals demonstrate that, comparing with the existing Time-Domain Blind Signal Processing methods, FDBD has more advantages and better prospects in mechanical fault detection.

Publisher

Trans Tech Publications, Ltd.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Rolling Bearing Fault Diagnosis under Strong Background Noise Based on ACMD and Optimized MOMEDA;Journal of Sensors;2024-01

2. Recursive Deconvolution Algorithm Based on Least Mean Square Error;2023 8th International Conference on Communication, Image and Signal Processing (CCISP);2023-11-17

3. Combined failure acoustical diagnosis based on improved frequency domain blind deconvolution;Journal of Physics: Conference Series;2012-05-28

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