Stochastic Resonance in a Multistable System Driven by Gaussian Noise

Shi, Pengfei; Pei, Li; An, Shujun; Han, Dongying

doi:10.1155/2016/1093562

Cited by 14 publications

(5 citation statements)

References 28 publications

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“…The CTSR system is driven by a weak periodic signal with Gaussian white noise and the model can be represented [28] by equation (1).…”

Section: Ctsr System Modelmentioning

confidence: 99%

Piecewise time-delay tri-stable stochastic resonance system and its application in bearing fault diagnosis

He,

Chen,

Zhang

2023

Phys. Scr.

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Current studies of time-delay based systems are compared to classical tri-stable stochastic resonance (CTSR) system and do not reflect the effect of adding a delay term on unsaturated systems. In this paper, a piecewise delayed tri-stable stochastic resonance (PTTSR) system is proposed. Firstly, the equivalent Langevin for PTTSR system is derived and compared with the output of CTSR and piecewise tri-stable stochastic resonance (PTSR) systems, where the addition of the time-delay term can further improve the output amplitude. Secondly, mean first pass time (MFPT) and output signal-to-noise ratio (SNR) are derived, and the effects of different parameters on MFPT and SNR are investigated. increasing the feedback intensity and time-delay length improved the output SNR. Then, the input period signal is then numerically simulated using the signal-to-noise ratio gain (SNRG) as a measurement, which increases by 2.75 dB for PTTSR system compared to PTSR system. Finally, the three systems are used for bearing fault detection and the system parameters are optimized by genetic algorithm. The results showed that the output amplitude of PTTSR system is more than 12 times that of PTSR system and the SNRG increased by more than 2dB. This demonstrates the superior performance of PTTSR system in detecting weak signals and provides good feasibility in engineering applications.

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“…The CTSR system is driven by a weak periodic signal with Gaussian white noise and the model can be represented [28] by equation (1).…”

Section: Ctsr System Modelmentioning

confidence: 99%

Piecewise time-delay tri-stable stochastic resonance system and its application in bearing fault diagnosis

He,

Chen,

Zhang

2023

Phys. Scr.

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show abstract

“…A particle is driven by a weak periodic signal and the Gaussian white noise in a tri-stable potential con-taining three potential wells and two potential barriers. The periodic oscillation can be enhanced when the noise is at a suitable level, which can be explained by the equation [22] dx dt…”

Section: Tri-stable Stochastic Resonance Modelmentioning

confidence: 99%

“…[13][14][15][16][17][18][19] Besides, SR systems with many new potential functions, like the tri-stable potential, were also explored by researchers. [20][21][22] However, for these existing SR models, the output SNR is not high enough and the detection effect is still not satisfactory. The detection effect of SR refers to the noise utilization or the enhancement extent on the periodic input signals, whose evaluation index is mainly the SNR or SNR gain of the system response tracking periodic input signal in this study.…”

Section: Introductionmentioning

confidence: 99%

Implication of two-coupled tri-stable stochastic resonance in weak signal detection

Liang

et al. 2018

Chinese Phys. B

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Stochastic resonance (SR) has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance (CTSSR) model is proposed to further increase the output signal-to-noise ratio (SNR) and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models. Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.

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“…That is, Benzi, an Italian scholar, used the phenomenon of "SR" in 1981 to study the nonlinear effects of ancient glaciers under the adiabatic approximation of bistable systems. The SR phenomenon in multistable systems driven by Gaussian noise was investigated theoretically and experimentally [11][12][13]. Many studies by using SR for weak signal detection have achieved positive research results [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Algorithm Used to Detect Weak Signals Covered by Noise in PIND

Wang

Liang

Xue

et al. 2019

International Journal of Aerospace Engineering

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Detection of the loose particles is urgently required in the spacecraft production processes. PIND (particle impact noise detection) is the most commonly used method for the detection of loose particles in the aerospace electronic components. However, when the mass of loose particles is smaller than 0.01 mg, the weak signals are difficult to be detected accurately. In this paper, the aperiodic stochastic resonance (ASR) is firstly used to detect weak signals of loose particles. The loose particle signal is simulated by the oscillation attenuation signal. The influences of structure parameters on the potential height and detection performance of ASR are studied by a numerical iteration method. The cross-correlation coefficient C 1 between input and output is chosen as a criterion for whether there is an existing a particle or not. Through normalization, the loose particle signal-labeled high frequency of 135 kHz is converted into the low-frequency band, which can be detected by the ASR method. According to the algorithm, weak signals covered by noise could be detected. The experimental results show that the detection accuracy is 66.7%. This algorithm improves the detection range of weak loose particle signals effectively.

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Stochastic Resonance in a Multistable System Driven by Gaussian Noise

Cited by 14 publications

References 28 publications

Piecewise time-delay tri-stable stochastic resonance system and its application in bearing fault diagnosis

Piecewise time-delay tri-stable stochastic resonance system and its application in bearing fault diagnosis

Implication of two-coupled tri-stable stochastic resonance in weak signal detection

Algorithm Used to Detect Weak Signals Covered by Noise in PIND

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