Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing
In order to detect the early weak faults of rolling bearings effectively,a method of adaptive stochastic resonance based on variational mode decomposition( VMD) is proposed. Firstly,the fault vibration signal of rolling bearing VMD is decomposed into finite Intrinsic Mode Functions( IMF),IMF compone...
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Format: | Article |
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Editorial Office of Journal of Mechanical Transmission
2018-01-01
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Series: | Jixie chuandong |
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Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.04.029 |
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author | Wang Zhixia Guo Li Cheng Qianqian |
author_facet | Wang Zhixia Guo Li Cheng Qianqian |
author_sort | Wang Zhixia |
collection | DOAJ |
description | In order to detect the early weak faults of rolling bearings effectively,a method of adaptive stochastic resonance based on variational mode decomposition( VMD) is proposed. Firstly,the fault vibration signal of rolling bearing VMD is decomposed into finite Intrinsic Mode Functions( IMF),IMF components containing fault feature are selected and reconstructed. The reconstructed signal is inputted into the stochastic resonance system,and the system structure parameters are optimized by the quantum particle swarm algorithm with the objective function of the improved weighted kurtosisso as to get the best resonance output,thus realizing the signal de-noising and enhance the fault feature. Finally the fault feature frequency is extracted by the autocorrelation envelope spectrum of output signal. The validity and advantages of the proposed method are proved by the analysis of the measured data. |
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spelling | doaj.art-df7d6ce829da4a4e9ca99a1c319dacaa2025-01-10T14:42:48ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014214414929936309Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling BearingWang ZhixiaGuo LiCheng QianqianIn order to detect the early weak faults of rolling bearings effectively,a method of adaptive stochastic resonance based on variational mode decomposition( VMD) is proposed. Firstly,the fault vibration signal of rolling bearing VMD is decomposed into finite Intrinsic Mode Functions( IMF),IMF components containing fault feature are selected and reconstructed. The reconstructed signal is inputted into the stochastic resonance system,and the system structure parameters are optimized by the quantum particle swarm algorithm with the objective function of the improved weighted kurtosisso as to get the best resonance output,thus realizing the signal de-noising and enhance the fault feature. Finally the fault feature frequency is extracted by the autocorrelation envelope spectrum of output signal. The validity and advantages of the proposed method are proved by the analysis of the measured data.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.04.029VMDAdaptive stochastic resonanceRolling bearing early fault detectionCharacteristic frequency |
spellingShingle | Wang Zhixia Guo Li Cheng Qianqian Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing Jixie chuandong VMD Adaptive stochastic resonance Rolling bearing early fault detection Characteristic frequency |
title | Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing |
title_full | Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing |
title_fullStr | Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing |
title_full_unstemmed | Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing |
title_short | Application of Bistable Stochastic Resonance based on VMD in Early Fault Detection of Rolling Bearing |
title_sort | application of bistable stochastic resonance based on vmd in early fault detection of rolling bearing |
topic | VMD Adaptive stochastic resonance Rolling bearing early fault detection Characteristic frequency |
url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.04.029 |
work_keys_str_mv | AT wangzhixia applicationofbistablestochasticresonancebasedonvmdinearlyfaultdetectionofrollingbearing AT guoli applicationofbistablestochasticresonancebasedonvmdinearlyfaultdetectionofrollingbearing AT chengqianqian applicationofbistablestochasticresonancebasedonvmdinearlyfaultdetectionofrollingbearing |