Acoustical Semi-blind Deconvolution for Bearing Defect Detection
Acoustical machine monitoring is frequently complicated by noisy environments at a production site. This paper presents a semi-blind deconvolution algorithm to extract only one desired acoustic source signal from different sources which are convoluted and mixed by mechanical systems before being mea...
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Series: | MATEC Web of Conferences |
Online Access: | http://dx.doi.org/10.1051/matecconf/20166301012 |
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doaj-d74188380a2c4c5186e2d9090c8ebe882021-02-02T06:12:27ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01630101210.1051/matecconf/20166301012matecconf_mmme2016_01012Acoustical Semi-blind Deconvolution for Bearing Defect DetectionWang Yu0Zhong Wen1Yin Yang2Liu Xiaoyin3Song Chunhua4Xihua University, School of Mechanical EngineeringXihua University, School of Mechanical EngineeringXihua University, School of Mechanical EngineeringXihua University, School of Mechanical EngineeringXihua University, School of Mechanical EngineeringAcoustical machine monitoring is frequently complicated by noisy environments at a production site. This paper presents a semi-blind deconvolution algorithm to extract only one desired acoustic source signal from different sources which are convoluted and mixed by mechanical systems before being measured. The method is based on blind model transformation, robust independent component analysis, reference signal and spectral distance. The new algorithm is tested on simulation and experimental cases. Results demonstrate that blind separation of acoustic signals is possible even when measurements are distanced from vibration exciting sources of faulty bearings. Furthermore, the method can eliminate the effect of structural resonances and large reverberation time of mixtures, which often causes severe problems in classical acoustical diagnostic methods of rolling element bearings.http://dx.doi.org/10.1051/matecconf/20166301012 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wang Yu Zhong Wen Yin Yang Liu Xiaoyin Song Chunhua |
spellingShingle |
Wang Yu Zhong Wen Yin Yang Liu Xiaoyin Song Chunhua Acoustical Semi-blind Deconvolution for Bearing Defect Detection MATEC Web of Conferences |
author_facet |
Wang Yu Zhong Wen Yin Yang Liu Xiaoyin Song Chunhua |
author_sort |
Wang Yu |
title |
Acoustical Semi-blind Deconvolution for Bearing Defect Detection |
title_short |
Acoustical Semi-blind Deconvolution for Bearing Defect Detection |
title_full |
Acoustical Semi-blind Deconvolution for Bearing Defect Detection |
title_fullStr |
Acoustical Semi-blind Deconvolution for Bearing Defect Detection |
title_full_unstemmed |
Acoustical Semi-blind Deconvolution for Bearing Defect Detection |
title_sort |
acoustical semi-blind deconvolution for bearing defect detection |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2016-01-01 |
description |
Acoustical machine monitoring is frequently complicated by noisy environments at a production site. This paper presents a semi-blind deconvolution algorithm to extract only one desired acoustic source signal from different sources which are convoluted and mixed by mechanical systems before being measured. The method is based on blind model transformation, robust independent component analysis, reference signal and spectral distance. The new algorithm is tested on simulation and experimental cases. Results demonstrate that blind separation of acoustic signals is possible even when measurements are distanced from vibration exciting sources of faulty bearings. Furthermore, the method can eliminate the effect of structural resonances and large reverberation time of mixtures, which often causes severe problems in classical acoustical diagnostic methods of rolling element bearings. |
url |
http://dx.doi.org/10.1051/matecconf/20166301012 |
work_keys_str_mv |
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1724301783024533504 |