IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS
In order to extract fault feature of signal. An improved blind deconvolution algorithm which based on generalized morphological filtering and improved KL distance clustering methods was proposed to deal with industrial field noise,multi interference sources and disadvantage of blind extraction algor...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2016-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.02.001 |
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| _version_ | 1841534775411605504 |
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| author | LIU Feng WU Xing PAN Nan ZHOU Jun |
| author_facet | LIU Feng WU Xing PAN Nan ZHOU Jun |
| author_sort | LIU Feng |
| collection | DOAJ |
| description | In order to extract fault feature of signal. An improved blind deconvolution algorithm which based on generalized morphological filtering and improved KL distance clustering methods was proposed to deal with industrial field noise,multi interference sources and disadvantage of blind extraction algorithm. First,the generalized morphological filter was used to extract the characteristic signal of observation signal. Then,the orthogonal matching pursuit algorithm was used to remove the period component of signal after being filtered. Finally,the improved KL distance was used to calculate distance of each component and obtain the separated signal by fuzzy C cluster. The results of computer simulation and real rolling bearing signals analysis show that this proposed method is quite effective. |
| format | Article |
| id | doaj-art-67aaeea2daec40f9b8995e729e8af4d3 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2016-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-67aaeea2daec40f9b8995e729e8af4d32025-01-15T02:37:01ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692016-01-013820721430594667IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSISLIU FengWU XingPAN NanZHOU JunIn order to extract fault feature of signal. An improved blind deconvolution algorithm which based on generalized morphological filtering and improved KL distance clustering methods was proposed to deal with industrial field noise,multi interference sources and disadvantage of blind extraction algorithm. First,the generalized morphological filter was used to extract the characteristic signal of observation signal. Then,the orthogonal matching pursuit algorithm was used to remove the period component of signal after being filtered. Finally,the improved KL distance was used to calculate distance of each component and obtain the separated signal by fuzzy C cluster. The results of computer simulation and real rolling bearing signals analysis show that this proposed method is quite effective.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.02.001Generalized morphological filteringCompressed sensingImproved KL distanceBlind signal processing |
| spellingShingle | LIU Feng WU Xing PAN Nan ZHOU Jun IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS Jixie qiangdu Generalized morphological filtering Compressed sensing Improved KL distance Blind signal processing |
| title | IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS |
| title_full | IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS |
| title_fullStr | IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS |
| title_full_unstemmed | IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS |
| title_short | IMPROVED TIME DOMAIN BLIND DECONVOLUTION ALGORITHM IN BEARING FAULT DIAGNOSIS |
| title_sort | improved time domain blind deconvolution algorithm in bearing fault diagnosis |
| topic | Generalized morphological filtering Compressed sensing Improved KL distance Blind signal processing |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2016.02.001 |
| work_keys_str_mv | AT liufeng improvedtimedomainblinddeconvolutionalgorithminbearingfaultdiagnosis AT wuxing improvedtimedomainblinddeconvolutionalgorithminbearingfaultdiagnosis AT pannan improvedtimedomainblinddeconvolutionalgorithminbearingfaultdiagnosis AT zhoujun improvedtimedomainblinddeconvolutionalgorithminbearingfaultdiagnosis |