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

Bearing is among the most widely used components in rotating machinery. Its failure can cause serious economic losses or even disasters. However, the fault-induced impulses are weak especially for the early failure. As to the bearing fault diagnosis, a novel bearing diagnosis method based on scale-varying fractional-order stochastic resonance (SFrSR) is proposed. Signal-to-noise ratio of the SFrSR output is regarded as the criterion for evaluating the stochastic resonance (SR) output. In the proposed method, by selecting the proper parameters (integration step [Formula: see text], amplitude gain [Formula: see text] and fractional-order [Formula: see text]) of SFrSR, the weak fault-induced impulses, the noise and the potential can be matched with each other. An optimal fractional-order dynamic system can be generated. To verify the proposed SFrSR, numerical tests and application verification are conducted in comparison with the traditional scale-varying first-order SR (SFiSR). The results prove that the parameters [Formula: see text] and [Formula: see text] affect the SFrSR effect seriously and the proposed SFrSR can enhance the weak signal while suppressing the noise. The SFrSR is more effective for bearing fault diagnosis than SFiSR.

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

Mathematical Problems in Engineering ◽

10.1155/2021/8888079 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Baochen Li ◽

Rui Tong ◽

Jianshe Kang ◽

Kuo Chi

Keyword(s):

Fault Diagnosis ◽

Power Spectrum ◽

Correlation Coefficient ◽

Stochastic Resonance ◽

Signal To Noise Ratio ◽

Quantitative Index ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Better Than

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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Weak Fault Enhancement Method for Bearing Fault Diagnosis by Using MWS Stochastic Resonance

Proceedings of IncoME-V & CEPE Net-2020 - Mechanisms and Machine Science ◽

10.1007/978-3-030-75793-9_51 ◽

2021 ◽

pp. 541-549

Author(s):

Chao Zhang ◽

Haoran Duan ◽

Jianguo Wang ◽

Fengshou Gu ◽

Biao Zhang

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Enhancement Method ◽

Weak Fault

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Stochastic Resonance with a Joint Woods-Saxon and Gaussian Potential for Bearing Fault Diagnosis

Mathematical Problems in Engineering ◽

10.1155/2014/315901 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 8

Author(s):

Haibin Zhang ◽

Qingbo He ◽

Siliang Lu ◽

Fanrang Kong

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Potential Model ◽

Signal To Noise Ratio ◽

Weak Signal Detection ◽

Gaussian Potential ◽

System Parameters ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Proposed Model

This work aims for a new stochastic resonance (SR) model which performs well in bearing fault diagnosis. Different from the traditional bistable SR system, we realize the SR based on the joint of Woods-Saxon potential (WSP) and Gaussian potential (GP) instead of a reflection-symmetric quartic potential. With this potential model, all the parameters in the Woods-Saxon and Gaussian SR (WSGSR) system are not coupled when compared to the traditional one, so the output signal-to-noise ratio (SNR) can be optimized much more easily by tuning the system parameters. Besides, a smoother potential bottom and steeper potential wall lead to a stable particle motion within each potential well and avoid the unexpected noise. Different from the SR with only WSP which is a monostable system, we improve it into a bistable one as a general form offering a higher SNR and a wider bandwidth. Finally, the proposed model is verified to be outstanding in weak signal detection for bearing fault diagnosis and the strategy offers us a more effective and feasible diagnosis conclusion.

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Research on Stochastic Resonance Method Based on Bee Colony Algorithm and its Application to Bearing Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.374 ◽

2014 ◽

Vol 548-549 ◽

pp. 374-378 ◽

Cited By ~ 2

Author(s):

Xiang Huan Cui ◽

Yong Ying Jiang ◽

Hai Feng Gao ◽

Jia Wei Xiang

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Signal To Noise Ratio ◽

Resonance Method ◽

Bearing Fault Diagnosis ◽

Bee Colony ◽

Parameters Selection ◽

Input Signals ◽

Diagnosis Method ◽

System Output

The background noise makes it difficult to detect incipient faults through vibration analysis. The stochastic resonance (SR) method can be applied to enhance the signal-to-noise ratio (SNR) of a system output using the unavoidable environmental noise. The parameters selection is the most important to generate SR. The proposed fault diagnosis method utilizes the artificial bee colony algorithm to find the best parameters of SR so as to match input signals and detect faults. The performance of the proposed method is confirmed as compared to the fixed parameters method.

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