Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings
Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge o...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3860 |
| _version_ | 1797603542103490560 |
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| author | Min Xu Chao Zheng Kelei Sun Li Xu Zijian Qiao Zhihui Lai |
| author_facet | Min Xu Chao Zheng Kelei Sun Li Xu Zijian Qiao Zhihui Lai |
| author_sort | Min Xu |
| collection | DOAJ |
| description | Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings. |
| first_indexed | 2024-03-11T04:32:37Z |
| format | Article |
| id | doaj.art-022e5336802146848167662c20499059 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:37Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-022e5336802146848167662c204990592023-11-17T21:15:38ZengMDPI AGSensors1424-82202023-04-01238386010.3390/s23083860Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of BearingsMin Xu0Chao Zheng1Kelei Sun2Li Xu3Zijian Qiao4Zhihui Lai5Ningbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, ChinaNingbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, ChinaNingbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, ChinaNingbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, ChinaSchool of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, ChinaShenzhen Key Laboratory of High Performance Nontraditional Manufacturing, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaAlthough stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings.https://www.mdpi.com/1424-8220/23/8/3860stochastic resonanceweak fault detectionmechanical fault diagnosis |
| spellingShingle | Min Xu Chao Zheng Kelei Sun Li Xu Zijian Qiao Zhihui Lai Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings Sensors stochastic resonance weak fault detection mechanical fault diagnosis |
| title | Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings |
| title_full | Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings |
| title_fullStr | Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings |
| title_full_unstemmed | Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings |
| title_short | Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings |
| title_sort | stochastic resonance with parameter estimation for enhancing unknown compound fault detection of bearings |
| topic | stochastic resonance weak fault detection mechanical fault diagnosis |
| url | https://www.mdpi.com/1424-8220/23/8/3860 |
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