A novel underdamped continuous unsaturation bistable stochastic resonance method and its application

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

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A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Symmetry ◽

10.3390/sym11080965 ◽

2019 ◽

Vol 11 (8) ◽

pp. 965 ◽

Cited By ~ 3

Author(s):

Lu Lu ◽

Yu Yuan ◽

Heng Wang ◽

Xing Zhao ◽

Jianjie Zheng

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Fitness Function ◽

Resonance Method ◽

Poor Performance ◽

Rolling Bearing ◽

Second Order ◽

Weak Signals ◽

Rolling Bearings ◽

Sr Method

Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

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A Novel Adaptive Stochastic Resonance Method Based on Tristable System and its Applications

Fluctuation and Noise Letters ◽

10.1142/s0219477521500048 ◽

2020 ◽

pp. 2150004

Author(s):

Gang Zhang ◽

Chuan Jiang ◽

Tian Qi Zhang

Keyword(s):

Signal Detection ◽

Stochastic Resonance ◽

Piecewise Linear ◽

Correlation Coefficients ◽

Resonance Method ◽

Resonance System ◽

Signal Energy ◽

Noise Energy ◽

Novel Model ◽

Output Snr

Stochastic resonance systems have the advantages of converting noise energy into signal energy, and have great potential in the field of signal detection and extraction. Aiming at the problems of the performance of classical stochastic resonance system whose model is not perfect enough and the correlation coefficients between parameters is too large to be optimized by algorithm, then a novel model of the tristable potential stochastic resonance system is proposed. The output SNR formula of the model is derived and analyzed, and the influence of its parameters on the model is clarified. Compared with the piecewise linear model by numerical simulation, the correctness of the formula and the superiority of the model are verified. Finally, the model and the classical tristable model are applied to bearing fault detection in which the genetic algorithm is used to optimize the parameters of the two systems. The results show that the model has better detection effects, which prove that the model has a strong potential in the field of signal detection.

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Weak-fault diagnosis using state-transition-algorithm-based adaptive stochastic-resonance method

Journal of Central South University ◽

10.1007/s11771-019-4123-6 ◽

2019 ◽

Vol 26 (7) ◽

pp. 1910-1920 ◽

Cited By ~ 3

Author(s):

Jin-tian Yin ◽

Yong-fang Xie ◽

Zhi-wen Chen ◽

Tao Peng ◽

Chun-hua Yang

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

State Transition ◽

Resonance Method ◽

State Transition Algorithm ◽

Weak Fault

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Application of continuous potential function stochastic resonance in early fault diagnosis of rolling bearings

Measurement and Control ◽

10.1177/0020294019890633 ◽

2020 ◽

Vol 53 (5-6) ◽

pp. 767-777

Author(s):

Xueping Ren ◽

Jian Kang ◽

Zhixing Li ◽

Jianguo Wang

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Potential Function ◽

Output Signal ◽

Signal To Noise Ratio ◽

Resonance Method ◽

Signal To Noise ◽

Rolling Bearings ◽

Continuous Potential ◽

Potential Parameters

The early fault signal of rolling bearings is very weak, and when analyzed under strong background noise, the traditional signal processing method is not ideal. To extract fault characteristic information more clearly, the second-order UCPSR method is applied to the early fault diagnosis of rolling bearings. The continuous potential function itself is a continuous sinusoidal function. The particle transition is smooth and the output is better. Because of its three parameters, the potential structure is more comprehensive and has more abundant characteristics. When the periodic signal, noise and potential function are the best match, the system exhibits better denoise compared to that of other methods. This paper discusses the influence of potential parameters on the motion state of particles between potential wells in combination with the potential parameter variation diagrams discussed. Then, the formula of output signal-to-noise ratio is derived to further study the relationships among potential parameters, and then the ant colony algorithm is used to optimize potential parameters in order to obtain the optimal output signal-to-noise ratio. Finally, an early weak fault diagnosis method for bearings based on the underdamped continuous potential stochastic resonance model is proposed. Through simulation and experimental verification, the underdamped continuous potential stochastic resonance results are compared with those of the time-delayed feedback stochastic resonance method, which proves the validity of the underdamped continuous potential stochastic resonance method.

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