A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection
In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals...
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| Формат: | Статья |
| Язык: | English |
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MDPI AG
2021-06-01
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| Серии: | Applied Sciences |
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| Online-ссылка: | https://www.mdpi.com/2076-3417/11/12/5385 |
| _version_ | 1827690126707261440 |
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| author | Hongjiang Cui Ying Guan Huayue Chen Wu Deng |
| author_facet | Hongjiang Cui Ying Guan Huayue Chen Wu Deng |
| author_sort | Hongjiang Cui |
| collection | DOAJ |
| description | In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the <i>SNR</i> is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery. |
| first_indexed | 2024-03-10T10:32:02Z |
| format | Article |
| id | doaj.art-3bc58bce04a747cbae0c39cd0f155414 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:32:02Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3bc58bce04a747cbae0c39cd0f1554142023-11-21T23:32:09ZengMDPI AGApplied Sciences2076-34172021-06-011112538510.3390/app11125385A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault DetectionHongjiang Cui0Ying Guan1Huayue Chen2Wu Deng3School of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Computer Science, China West Normal University, Nanchong 637002, ChinaSchool of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaIn recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the <i>SNR</i> is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.https://www.mdpi.com/2076-3417/11/12/5385motor bearingssignal processingfault detectionstochastic resonancesignal-to-noise ratioseeker optimization |
| spellingShingle | Hongjiang Cui Ying Guan Huayue Chen Wu Deng A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection Applied Sciences motor bearings signal processing fault detection stochastic resonance signal-to-noise ratio seeker optimization |
| title | A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection |
| title_full | A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection |
| title_fullStr | A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection |
| title_full_unstemmed | A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection |
| title_short | A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection |
| title_sort | novel advancing signal processing method based on coupled multi stable stochastic resonance for fault detection |
| topic | motor bearings signal processing fault detection stochastic resonance signal-to-noise ratio seeker optimization |
| url | https://www.mdpi.com/2076-3417/11/12/5385 |
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