The Characteristic Analysis and Application of a Novel Time-Delay Feedback Piecewise Tri-stable Stochastic Resonance System

Abstract This paper proposes the stiffness nonlinearities and asymmetric SD (smooth and discontinuous) oscillators under time-delay feedback control with a fractional damping. With the effect of displacement and velocity feedback, the oscillator is adjusted to the desired vibration state and then the stochastic resonance (SR) is achieved. This article discusses the contribution of various system parameters and time-delay feedback to SR, especially which induced by fractional damping. It should be noted that this paper provides effective guidance for fault diagnosis and weak signal detection, energy harvesting, vibration isolation and vibration reduction.

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Low-Power-Consumption Physical Reservoir Computing Model Based on Overdamped Bistable Stochastic Resonance System

Neurocomputing ◽

10.1016/j.neucom.2021.09.074 ◽

2021 ◽

Author(s):

Zhiqiang Liao ◽

Zeyu Wang ◽

Hiroyasu Yamahara ◽

Hitoshi Tabata

Keyword(s):

Power Consumption ◽

Low Power ◽

Stochastic Resonance ◽

Low Power Consumption ◽

Reservoir Computing ◽

Resonance System ◽

Computing Model ◽

Model Based

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NovelWoods–Saxon stochastic resonance system for weak signal detection

Chinese Physics B ◽

10.1088/1674-1056/ab75ca ◽

2020 ◽

Vol 29 (4) ◽

pp. 040503 ◽

Cited By ~ 1

Author(s):

Yong-Hui Zhou ◽

Xue-Mei Xu ◽

Lin-Zi Yin ◽

Yi-Peng Ding ◽

Jia-Feng Ding ◽

...

Keyword(s):

Signal Detection ◽

Stochastic Resonance ◽

Weak Signal ◽

Weak Signal Detection ◽

Resonance System

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Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Complexity ◽

10.1155/2020/8817814 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Lina He ◽

Chuan Jiang

Keyword(s):

Genetic Algorithm ◽

Fault Detection ◽

Stochastic Resonance ◽

Signal To Noise Ratio ◽

Piecewise Linear ◽

Poor Performance ◽

System Model ◽

Adaptive Genetic Algorithm ◽

Resonance System ◽

Noise Energy

The stochastic resonance system has the advantage of making the noise energy transfer to the signal energy. Because the existing stochastic resonance system model has the problem of poor performance, an asymmetric piecewise linear stochastic resonance system model is proposed, and the parameters of the model are optimized by a genetic algorithm. The signal-to-noise ratio formula of the model is derived and analyzed, and the theoretical basis for better performance of the model is given. The influence of the asymmetric coefficient on system performance is studied, which provides guidance for the selection of initial optimization range when a genetic algorithm is used. At the same time, the formula is verified and analyzed by numerical simulation, and the correctness of the formula is proved. Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system. The results show that the model has an excellent detection effect, which proves that the model has great potential in fault detection.

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Stochastic resonance in asymmetric time-delayed bistable system under multiplicative and additive noise and its applications in bearing fault detection

Modern Physics Letters B ◽

10.1142/s021798491950341x ◽

2019 ◽

Vol 33 (28) ◽

pp. 1950341 ◽

Cited By ~ 3

Author(s):

Lifang He ◽

Dayun Hu ◽

Gang Zhang ◽

Siliang Lu

Keyword(s):

Time Delay ◽

Stochastic Resonance ◽

Additive Noise ◽

Random Force ◽

Small Time ◽

Delayed Feedback ◽

Bistable System ◽

Bearing Fault ◽

Strength Formula ◽

Time Delayed Feedback

The asymmetric bistable system with time delays in the feedback force and random force under multiplicative and additive Gaussian noise is studied. Using the small time delay approximation approach and time-delayed Fokker–Planck equations (FPE), the signal-to-noise ratio (SNR) of the proposed stochastic system is obtained. The stochastic resonance (SR) phenomena influenced by parameters — including system parameters [Formula: see text], [Formula: see text], asymmetry parameter [Formula: see text], time delay [Formula: see text], strength [Formula: see text] of the time-delayed feedback, noise intensities [Formula: see text] and [Formula: see text] of multiplicative and additive noise, and correlation strength [Formula: see text] between two noises, are also analyzed by numerical simulations. Results demonstrate that the SR performance of the asymmetric bistable system is superior to one symmetric bistable system. Besides, both time delay and strength of time-delayed feedback could enhance the SR to some extent. Then, the asymmetric time-delayed bistable SR (ATDBSR) method is used to the bearing fault diagnosis. The engineering applications of the ATDBSR method are realized and the value of the method is verified by effective experimental results.

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Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance

Applied Sciences ◽

10.3390/app10062048 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2048 ◽

Cited By ~ 1

Author(s):

Yang Jiang ◽

Bo He ◽

Jia Guo ◽

Pengfei Lv ◽

Xiaokai Mu ◽

...

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Ant Colony ◽

Resonance System ◽

Resonance Model ◽

Weak Fault

The autonomous underwater vehicle (AUV) is mainly used in the development and exploration of the ocean. As an important module of the AUV, the actuator plays an important role in the normal execution of the AUV. Therefore, the fault diagnosis of the actuator is particularly important. At present, the research on the strong faults, such as the winding of the actuator, has achieved good results, but the research on the weak fault diagnosis is relatively rare. In this paper, the tri-stable stochastic resonance model is analyzed, and the ant colony tri-stable stochastic resonance model is used to diagnose the weak fault. The system accurately diagnoses the fault of the actuator collision and verifies the adaptive tri-stable stochastic resonance system. This model has better diagnostic results than the bi-stable stochastic resonance system.

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