SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS

Weight-adjusted variant second order blind identification( WASOBI) algorithm had been used in fault diagnosis field,but it couldn’t separate source signal when the observation signal’s dimension was insufficient. This paper combined kernels with this algorithm to achieve underdetermined blind source...

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Main Authors: 刘邦, 肖涵, 易灿灿
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Strength 2018-01-01
Series:Jixie qiangdu
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Online Access:http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.05.005
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author 刘邦
肖涵
易灿灿
author_facet 刘邦
肖涵
易灿灿
author_sort 刘邦
collection DOAJ
description Weight-adjusted variant second order blind identification( WASOBI) algorithm had been used in fault diagnosis field,but it couldn’t separate source signal when the observation signal’s dimension was insufficient. This paper combined kernels with this algorithm to achieve underdetermined blind source separation,and applied it to multi-fault diagnosis. First,the collected single-channel signal was decomposed into multi-dimensional signal by kernels. Then the K-SVD source number estimation algorithm was used to estimate the number of sources. According to the estimated result,a positive definite matrix was reconstructed. Finally,weight-adjusted variant second order blind identification algorithm was applied to separate the source signals. The experiment results indicate that it can well diagnose the multi-fault on rolling bearing with single-channel mechanical signal,feasibility of this method is also validated.
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institution Kabale University
issn 1001-9669
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publishDate 2018-01-01
publisher Editorial Office of Journal of Mechanical Strength
record_format Article
series Jixie qiangdu
spelling doaj-art-8b99a236b77d4b4cb56b3d039bff28a52025-01-15T02:31:11ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692018-01-01401043104930602640SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS刘邦肖涵易灿灿Weight-adjusted variant second order blind identification( WASOBI) algorithm had been used in fault diagnosis field,but it couldn’t separate source signal when the observation signal’s dimension was insufficient. This paper combined kernels with this algorithm to achieve underdetermined blind source separation,and applied it to multi-fault diagnosis. First,the collected single-channel signal was decomposed into multi-dimensional signal by kernels. Then the K-SVD source number estimation algorithm was used to estimate the number of sources. According to the estimated result,a positive definite matrix was reconstructed. Finally,weight-adjusted variant second order blind identification algorithm was applied to separate the source signals. The experiment results indicate that it can well diagnose the multi-fault on rolling bearing with single-channel mechanical signal,feasibility of this method is also validated.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.05.005Single-channel signalWeight-adjusted variant second order blind identificationKernelsSource number estimationMulti-fault diagnosis
spellingShingle 刘邦
肖涵
易灿灿
SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS
Jixie qiangdu
Single-channel signal
Weight-adjusted variant second order blind identification
Kernels
Source number estimation
Multi-fault diagnosis
title SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS
title_full SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS
title_fullStr SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS
title_full_unstemmed SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS
title_short SECOND ORDER BLIND IDENTIFICATION OF SINGLE-CHANNEL SIGNAL BASED ON KERNELS
title_sort second order blind identification of single channel signal based on kernels
topic Single-channel signal
Weight-adjusted variant second order blind identification
Kernels
Source number estimation
Multi-fault diagnosis
url http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.05.005
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