Effect of scale-varying fractional-order stochastic resonance by simulation and its application in bearing diagnosis

Chi, Kuo; Kang, Jianshe; Zhang, Xinghui; Zhao, Fei

doi:10.1142/s1793962319410034

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Since the Italian researcher Benzi [15,16] proposed the stochastic resonance (SR) concept in 1981, SR has attracted the attention of many scholars and has been widely used in the fields of image enhancement [17,18], mechanical equipment fault diagnosis [19][20][21], wireless communication [22][23][24], etc.. There are some restrictions on using the method of SR [25], and scholars have studied many ways to overcome it.…”

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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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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“…However, the actual fault signals are mostly made up of large-parameter signals. Currently, the signal can be preprocessed through methods as modulation and demodulation, 17 secondary sampling, 18 and frequency shift scaling, 19,20 so that the signal can meet the necessary small-parameter requirements.…”

Section: Introductionmentioning

confidence: 99%

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

Ren

Kang

et al. 2020

Measurement and Control

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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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Planetary Gearbox Diagnosis Using Varying-stable Stochastic Resonance

Kang

Chi

Jing³

et al. 2019

2019 Prognostics and System Health Management Conference (PHM-Qingdao)

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Effect of scale-varying fractional-order stochastic resonance by simulation and its application in bearing diagnosis

Cited by 3 publications

References 31 publications

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

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

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

Planetary Gearbox Diagnosis Using Varying-stable Stochastic Resonance

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