Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation
The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurr...
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Elsevier
2025-06-01
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| Series: | Results in Physics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211379725001779 |
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| author | Tiantian Hou Shangbin Jiao Yi Wang Nianlong Song Jianghua Li Wenchuan Cui |
| author_facet | Tiantian Hou Shangbin Jiao Yi Wang Nianlong Song Jianghua Li Wenchuan Cui |
| author_sort | Tiantian Hou |
| collection | DOAJ |
| description | The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system’s dynamic response. The system’s non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm’s ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system’s performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance.This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and significantly enhance the signal-to-noise ratio (SNR) of weak signals under the same noise intensity. |
| format | Article |
| id | doaj-art-3349a1fedf5f4236bfbe712c579a5049 |
| institution | DOAJ |
| issn | 2211-3797 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Physics |
| spelling | doaj-art-3349a1fedf5f4236bfbe712c579a50492025-08-20T02:57:53ZengElsevierResults in Physics2211-37972025-06-017310828310.1016/j.rinp.2025.108283Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulationTiantian Hou0Shangbin Jiao1Yi Wang2Nianlong Song3Jianghua Li4Wenchuan Cui5Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, PR ChinaCorresponding author.; Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, PR ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, PR ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, PR ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, PR ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, PR ChinaThe theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system’s dynamic response. The system’s non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm’s ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system’s performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance.This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and significantly enhance the signal-to-noise ratio (SNR) of weak signals under the same noise intensity.http://www.sciencedirect.com/science/article/pii/S2211379725001779Coupled stochastic resonanceIndependent parameter regulationSPDMFPTBearing fault detection |
| spellingShingle | Tiantian Hou Shangbin Jiao Yi Wang Nianlong Song Jianghua Li Wenchuan Cui Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation Results in Physics Coupled stochastic resonance Independent parameter regulation SPD MFPT Bearing fault detection |
| title | Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation |
| title_full | Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation |
| title_fullStr | Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation |
| title_full_unstemmed | Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation |
| title_short | Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation |
| title_sort | research and application of gaussian coupled control bistable stochastic resonance system with independent parameter regulation |
| topic | Coupled stochastic resonance Independent parameter regulation SPD MFPT Bearing fault detection |
| url | http://www.sciencedirect.com/science/article/pii/S2211379725001779 |
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