Note: Signal amplification and filtering with a tristable stochastic resonance cantilever

Lu, Siliang; He, Qingbo; Zhang, Haibin; Zhang, Shangbin; Kong, Fanrang

doi:10.1063/1.4792603

Cited by 43 publications

(15 citation statements)

References 12 publications

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“…Stochastic resonance is regarded as a method of weak signal detection [11][12][13][14] since it was firstly proposed to express the periodic alternations between the Earth's ice age and the warm climate period in 1981 [15]. Later, various potential function models of stochastic resonance were proposed based on the model of traditional bistable stochastic resonance [16,17], such as monostable models [18][19][20], tristable models [21][22][23], and two metastable models [24].…”

Section: Introductionmentioning

confidence: 99%

Performance Investigation of Stochastic Resonance in Three Types of Asymmetric Bistable System Driven by Trichotomous Noise

Zhao

Liu

et al. 2020

Complexity

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This paper proposes a new system whose potential function is with three types of asymmetric potential wells, driven by trichotomous noise. Firstly, the three types of asymmetric bistable system are described in detail, and the changes of asymmetric bistable system potential function under different asymmetric factors are analyzed. Secondly, the effect of potential function parameters, asymmetric factor α , noise intensity D, and the probability of particle transition q is discussed, using numerical simulation. The detection effects of traditional symmetric SR and three types of asymmetric SR are observed and compared under the driving of trichotomous noise and periodic signals. The mean of signal-to-noise ratio gain is the indicator of the system's effectiveness on enhancing weak signal. The results indicate that it can make the detection effect of the asymmetric system better than that of the traditional bistable system by adjusting the parameters of the asymmetric stochastic resonance system and trichotomous noise.

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Section: Introductionmentioning

confidence: 99%

Performance Investigation of Stochastic Resonance in Three Types of Asymmetric Bistable System Driven by Trichotomous Noise

Zhao

Liu

et al. 2020

Complexity

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“…SR models express the SR phenomenon types such as overdamped SR [9], underdamped SR [10], cascaded SR [11], and parallel SR [12]. SR potentials express the SR nonlinear systems such as monostable potential [13], bistable potential [9][10][11][12], tristable potential [14], and multistable potential [15]. SR model and SR potential focus on the description of SR phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

Tong

Kang

et al. 2021

Mathematical Problems in Engineering

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Stochastic resonance is like a nonlinear filter to detect the weak bearing fault-induced impulses that submerged in strong noises. Signal-to-noise ratio (SNR) is often used as the index to evaluate the SR output, but the fault characteristic frequency (FCF) must be known in order to calculate SNR. A novel bearing fault diagnosis method called synthetic quantitative index-based adaptive underdamped stochastic resonance (SQI-AUSR) is proposed. The synthetic quantitative index (SQI) is composed of power spectrum kurtosis, kurtosis, margin index, and correlation coefficient. The SQI is independent of FCF, which avoids the limitation that the calculation of SNR must know the FCF. Numeric simulations and two case studies of bearing faults are carried out. The results show that (1) the SQI is more effective than other proposed indexes such as correlation coefficient and weight power spectrum kurtosis and (2) the proposed SQI-AUSR is effective for bearing fault diagnosis and is better than SNR-AOSR.

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“…The above methods lay a foundation for the application of SR in the field of mechanical fault diagnosis. With the development of SR technology, some new potential function models have been proposed to improve the effect of SR, for example, tri-stable model, 16,17 mono-stable model, 18 and multi-scale SR array model. 19 The above SR models basically adopt the traditional first-order overdamped SR model.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric second-order stochastic resonance weak fault feature extraction method

Tang

Shi

2020

Measurement and Control

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To solve the problem that the weak fault signal is difficult to extract under strong background noise, an asymmetric second-order stochastic resonance method is proposed. By adjusting the damping factor and the asymmetry, weak signals, noise, and potential wells are matched to each other to achieve the best stochastic resonance state so that weak fault characteristics can be effectively extracted in strong background noise. Under adiabatic approximation, the effects of damping coefficient, noise intensity, and asymmetry on the output signal-to-noise ratio are discussed based on the two-state model theory. Under the same parameters, the output signal-to-noise ratio of the asymmetric second-order stochastic resonance system is better than that of the underdamped second-order stochastic resonance system. The bearing fault and field engineering experimental results are provided to justify the comparative advantage of the proposed method over the underdamped second-order stochastic resonance method.

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Note: Signal amplification and filtering with a tristable stochastic resonance cantilever

Cited by 43 publications

References 12 publications

Performance Investigation of Stochastic Resonance in Three Types of Asymmetric Bistable System Driven by Trichotomous Noise

Performance Investigation of Stochastic Resonance in Three Types of Asymmetric Bistable System Driven by Trichotomous Noise

Bearing Fault Diagnosis Using Synthetic Quantitative Index-Based Adaptive Underdamped Stochastic Resonance

Asymmetric second-order stochastic resonance weak fault feature extraction method

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