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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1001-9669 |