Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing
The blind deconvolution based on cyclostationarity maximization （CYCBD） is applied to solve the problem that the periodic transient impacts is not obvious at the initial stage of rolling bearing failure under background noise. The noise reduction effect of CYCBD around the filter length and cycle fr...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2021-02-01
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| Series: | Jixie chuandong |
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| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.026 |
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| author | Yutao Liu Huer Sun |
| author_facet | Yutao Liu Huer Sun |
| author_sort | Yutao Liu |
| collection | DOAJ |
| description | The blind deconvolution based on cyclostationarity maximization （CYCBD） is applied to solve the problem that the periodic transient impacts is not obvious at the initial stage of rolling bearing failure under background noise. The noise reduction effect of CYCBD around the filter length and cycle frequency， particle swarm optimization algorithm （PSO） is applied to intelligently optimize CYCBD. Determine the optimal parameters to solve the instability of CYCBD. Firstly， PSO is used to optimize the filter length and cycle frequency in CYCBD to enhance the periodic impacts component. Then， the optimal solution of filter length and cycle frequency is iteratively found by using crest factor of envelope spectrum（EC） as the objective function of PSO. Finally， by applying the optimal solution to CYCBD and conducting envelope demodulation analysis on the enhanced signals， the fault characteristic frequency of bearing signals can be accurately obtained. Through the analysis of simulation signals and experimental data， it is shown that the method can effectively enhance the periodic transient impacts characteristics of vibration signals and has advantages in the early fault feature extraction of rolling bearings. |
| format | Article |
| id | doaj-art-491db54bbaa3419ca108f6c99c314918 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2021-02-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-491db54bbaa3419ca108f6c99c3149182025-01-10T14:54:12ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392021-02-014517117629799987Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling BearingYutao LiuHuer SunThe blind deconvolution based on cyclostationarity maximization （CYCBD） is applied to solve the problem that the periodic transient impacts is not obvious at the initial stage of rolling bearing failure under background noise. The noise reduction effect of CYCBD around the filter length and cycle frequency， particle swarm optimization algorithm （PSO） is applied to intelligently optimize CYCBD. Determine the optimal parameters to solve the instability of CYCBD. Firstly， PSO is used to optimize the filter length and cycle frequency in CYCBD to enhance the periodic impacts component. Then， the optimal solution of filter length and cycle frequency is iteratively found by using crest factor of envelope spectrum（EC） as the objective function of PSO. Finally， by applying the optimal solution to CYCBD and conducting envelope demodulation analysis on the enhanced signals， the fault characteristic frequency of bearing signals can be accurately obtained. Through the analysis of simulation signals and experimental data， it is shown that the method can effectively enhance the periodic transient impacts characteristics of vibration signals and has advantages in the early fault feature extraction of rolling bearings.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.026Rolling bearingBlind deconvolution based on cyclostationarity maximizationParticle swarm optimization algorithmFilter lengthCycle frequency |
| spellingShingle | Yutao Liu Huer Sun Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing Jixie chuandong Rolling bearing Blind deconvolution based on cyclostationarity maximization Particle swarm optimization algorithm Filter length Cycle frequency |
| title | Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing |
| title_full | Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing |
| title_fullStr | Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing |
| title_full_unstemmed | Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing |
| title_short | Study on Application of CYCBD based on PSO in Fault Feature Extraction of Rolling Bearing |
| title_sort | study on application of cycbd based on pso in fault feature extraction of rolling bearing |
| topic | Rolling bearing Blind deconvolution based on cyclostationarity maximization Particle swarm optimization algorithm Filter length Cycle frequency |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2021.02.026 |
| work_keys_str_mv | AT yutaoliu studyonapplicationofcycbdbasedonpsoinfaultfeatureextractionofrollingbearing AT huersun studyonapplicationofcycbdbasedonpsoinfaultfeatureextractionofrollingbearing |