Asymmetric delay feedback stochastic resonance detection method based on prior knowledge particle swarm optimization

Tang, Jiawei; Shi, Boqiang; Li, Zhixing

doi:10.1016/j.cjph.2018.08.019

Cited by 19 publications

(10 citation statements)

References 21 publications

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“…Most systems in nature are asymmetric, and the research on asymmetric SR systems has received more and more attention from scholars. [23][24][25][26][27] Qiao et al 28 pointed out that under the asymmetric potential function and the symmetric potential function, the output SNR varies greatly. Li et al 29 optimized the shape of the asymmetric system to better detect the target frequency when subjected to a large amount of noise interference.…”

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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Section: Introductionmentioning

confidence: 99%

Asymmetric second-order stochastic resonance weak fault feature extraction method

Tang

Shi

2020

Measurement and Control

Self Cite

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“…Since time lags are widespread in nature, the study of SR with time-delayed feedback conforms to the developing trend. [28][29][30][31][32][33][34] Shi et al [28] derived the expression of the steadystate probability density function (SPD) and SNR of SR with time-delayed feedback, suggesting that the delay time and feedback strength of the time-delayed term can enhance the SNR. Introducing time-delayed terms into the potential func-tion can reduce the SNR, but the degradation reaction has the opposite effect.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric stochastic resonance under non-Gaussian colored noise and time-delayed feedback*

Shi

Sun

et al. 2020

Chinese Phys. B

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Based on adiabatic approximation theory, in this paper we study the asymmetric stochastic resonance system with time-delayed feedback driven by non-Gaussian colored noise. The analytical expressions of the mean first-passage time (MFPT) and output signal-to-noise ratio (SNR) are derived by using a path integral approach, unified colored-noise approximation (UCNA), and small delay approximation. The effects of time-delayed feedback and non-Gaussian colored noise on the output SNR are analyzed. Moreover, three types of asymmetric potential function characteristics are thoroughly discussed. And they are well-depth asymmetry (DASR), well-width asymmetry (WASR), and synchronous action of well-depth and well-width asymmetry (DWASR), respectively. The conclusion of this paper is that the time-delayed feedback can suppress SR, however, the non-Gaussian noise deviation parameter has the opposite effect. Moreover, the correlation time plays a significant role in improving SNR, and the SNR of asymmetric stochastic resonance is higher than that of symmetric stochastic resonance. Our experiments demonstrate that the appropriate parameters can make the asymmetric stochastic resonance perform better to detect weak signals than the symmetric stochastic resonance, in which no matter whether these signals have low frequency or high frequency, accompanied by strong or weak noise.

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“…Moreover, [27] proposed a method of frequency-shifting scale to improve the detection accuracy of SR. Other scholars [28,29] have added different noises to achieve the purpose of enhancing the SR phenomenon. Though the use of different optimization algorithms to solve the problem of classical SR output local saturation and improve the detection ability of SR to obtain the best SNR [19,[30][31][32]. Researchers have also changed nonlinear systems, by establishing a lot of new SR potential function modes to realize SR, such as [33] has extended the harmonic potential of the linear random vibration system and gives a more general power function potential.…”

Section: Introductionmentioning

confidence: 99%

The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

Zhang

Duan

Xue³

et al. 2020

Energies

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As the critical parts of wind turbines, rolling bearings are prone to faults due to the extreme operating conditions. To avoid the influence of the faults on wind turbine performance and asset damages, many methods have been developed to monitor the health of bearings by accurately analyzing their vibration signals. Stochastic resonance (SR)-based signal enhancement is one of effective methods to extract the characteristic frequencies of weak fault signals. This paper constructs a new SR model, which is established based on the joint properties of both Power Function Type Single-Well and Woods-Saxon (PWS), and used to make fault frequency easy to detect. However, the collected vibration signals usually contain strong noise interference, which leads to poor effect when using the SR analysis method alone. Therefore, this paper combines the Fourier Decomposition Method (FDM) and SR to improve the detection accuracy of bearing fault signals feature. Here, the FDM is an alternative method of empirical mode decomposition (EMD), which is widely used in nonlinear signal analysis to eliminate the interference of low-frequency coupled signals. In this paper, a new stochastic resonance model (PWS) is constructed and combined with FDM to enhance the vibration signals of the input and output shaft of the wind turbine gearbox bearing, make the bearing fault signals can be easily detected. The results show that the combination of the two methods can detect the frequency of a bearing failure, thereby reminding maintenance personnel to urgently develop a maintenance plan.

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Asymmetric delay feedback stochastic resonance detection method based on prior knowledge particle swarm optimization

Cited by 19 publications

References 21 publications

Asymmetric second-order stochastic resonance weak fault feature extraction method

Asymmetric second-order stochastic resonance weak fault feature extraction method

Asymmetric stochastic resonance under non-Gaussian colored noise and time-delayed feedback*

The Enhancement of Weak Bearing Fault Signatures by Stochastic Resonance with a Novel Potential Function

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