A Novel Piecewise Symmetric Two-Dimensional Tri-stable Stochastic Resonance System for Bearing Fault Detection

Zhang, Gang; Jiang, Zhongjun

doi:10.1007/s42417-022-00785-5

Cited by 2 publications

(4 citation statements)

References 31 publications

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“…Through simulation and actual fault signal detection, Lu et al in [37] demonstrated the superiority of the proposed Woods-Saxon stochastic resonance (WSSR) method over the CBSR. Zhang et al proposed a combined hybrid TSR system by combining WSSR and CBSR with simulations to demonstrate its effectiveness [38]. However, the TSR suggested in [38] is unable to controllably change the potential structure since the potential well depth and width of the CBSR change simultaneously with a single parameter.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al proposed a combined hybrid TSR system by combining WSSR and CBSR with simulations to demonstrate its effectiveness [38]. However, the TSR suggested in [38] is unable to controllably change the potential structure since the potential well depth and width of the CBSR change simultaneously with a single parameter. The two TSR models mentioned above have a significant impact on the potential structure when a parameter is changed [36,38].…”

Section: Introductionmentioning

confidence: 99%

“…However, the TSR suggested in [38] is unable to controllably change the potential structure since the potential well depth and width of the CBSR change simultaneously with a single parameter. The two TSR models mentioned above have a significant impact on the potential structure when a parameter is changed [36,38]. Hence, building a novel controlled potential structure with an optimal control strategy to catch weak signals remains a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

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Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection

Liu

Guo

et al. 2023

Sensors

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Weak fault detection with stochastic resonance (SR) is distinct from conventional approaches in that it is a nonlinear optimal signal processing to transfer noise into the signal, resulting in a higher output SNR. Owing to this special characteristic of SR, this study develops a controlled symmetry with Woods-Saxon stochastic resonance (CSwWSSR) model based on the Woods-Saxon stochastic resonance (WSSR), where each parameter of the model may be modified to vary the potential structure. Then, the potential structure of the model is investigated in this paper, along with the mathematical analysis and experimental comparison to clarify the effect of each parameter on it. The CSwWSSR is a tri-stable stochastic resonance, but differs from others in that each of its three potential wells is controlled by different parameters. Moreover, the particle swarm optimization (PSO), which can quickly find the ideal parameter matching, is introduced to attain the optimal parameters of the CSwWSSR model. Fault diagnosis of simulation signals and bearings was carried out to confirm the viability of the proposed CSwWSSR model, and the results revealed that the CSwWSSR model is superior to its constituent models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection

Liu

Guo

et al. 2023

Sensors

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“…Stochastic resonance (SR) was first presented by Italian scholar R. Benzi in the article about alternation phenomenon of glaciation and heating period of Earth [5]. e stochastic resonance model has been widely applied in the weak signal detection, which has obtained abundant research result [6][7][8]. Zhao et al proposed an adaptive stochastic resonance (SR) method based on the grey wolf optimizer algorithm for fast pulsar identification [9].…”

Section: Introductionmentioning

confidence: 99%

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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Because the output signal of the time grid sensor is so weak and is easily affected by the disturbing noise, it is difficult to extract the induction signal containing the position information, when the time grid sensor is used to detect the rotor position of the permanent magnet synchronous motor (PMSM). In this paper, an adaptive monostable stochastic resonance method is proposed to realize the weak signal detection of the time grid sensor. The tunnel magnetoresistance effect sensor (TMR) is adopted to detect the change of the rotor magnetic field of the motor in this method, and the detection model of the time grid sensor about PMSM is constructed. The monostable stochastic resonance theory is applied to extract the induction signal of the time grid sensor. As to the problem of the tuning the parameters of the monostable stochastic resonance system, the genetic algorithm is adopted to optimize the parameters of the monostable stochastic resonance system. The experimental results show that the monostable stochastic resonance method based on the genetic algorithm can effectively detect the weak induction signal of the time grid sensor about PMSM. This method solves the problem of the weak signal extraction of the time grid sensor for PMSM, and the detection accuracy is also improved.

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A Novel Piecewise Symmetric Two-Dimensional Tri-stable Stochastic Resonance System for Bearing Fault Detection

Cited by 2 publications

References 31 publications

Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection

Controlled Symmetry with Woods-Saxon Stochastic Resonance Enabled Weak Fault Detection

Research on the Weak Signal Extraction Method with Adaptive Stochastic Resonance Based on the Time Grid Sensor for PMSM

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