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

2014 ◽  
Vol 548-549 ◽  
pp. 374-378 ◽  
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.

scholarly journals Rotating Elements Fault Diagnosis Method Based on Stochastic Resonance with Triple-well Potential System of Genetic Algorithm

2020 ◽  
Author(s):  
Liu ZiWen ◽  
Bao JinSong ◽  
Xiao Lei ◽  
Wang BoBo
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Engineering Application ◽  
Resonance Method ◽  
Rotating Body ◽  
Potential System ◽  
Diagnosis Method

Abstract In engineering applications, the fault signal of rotating elements is easily submerged in the background noise. In order to solve this problem, a rotating body fault diagnosis method based on stochastic resonance with triple-well potential system of genetic algorithm is proposed. In this method, the signal-to-noise ratio(SNR) of the Triple-well potential system is used as the fitness function of the genetic algorithm, and several parameters of the system are optimized at the same time, which effectively improves the feature extraction effect of the weak fault of the rotating body. The results of simulation and engineering experiments show that this method has better detection effect than bistable stochastic resonance method, and can effectively detect the fault signal submerged by noise, which has a good engineering application prospect.

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

2019 ◽  
Vol 10 (01) ◽  
pp. 1941003 ◽  
Author(s):  
Kuo Chi ◽  
Jianshe Kang ◽  
Xinghui Zhang ◽  
Fei Zhao
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fractional Order ◽  
Signal To Noise Ratio ◽  
Economic Losses ◽  
Integration Step ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault

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.

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

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.

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

2020 ◽  
Vol 53 (5-6) ◽  
pp. 767-777
Author(s):  
Xueping Ren ◽  
Jian Kang ◽  
Zhixing Li ◽  
Jianguo Wang
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Potential Function ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Signal To Noise ◽  
Rolling Bearings ◽  
Continuous Potential ◽  
Potential Parameters

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.

Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

2017 ◽  
Vol 391 ◽  
pp. 194-210 ◽  
Author(s):  
Peng Zhou ◽  
Siliang Lu ◽  
Fang Liu ◽  
Yongbin Liu ◽  
Guihua Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Resonance Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Synthetic Index

scholarly journals Research and application of weak fault diagnosis method based on asymmetric potential stochastic resonance

2019 ◽  
Vol 52 (5-6) ◽  
pp. 625-633 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi ◽  
Xueping Ren ◽  
Wenyan Zhu
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Bearing Failure ◽  
Asymmetric System ◽  
Symmetric Potential ◽  
Frequency Detection ◽  
Detection Technology ◽  
Potential Structure

Because fault characteristics are often difficult to extract from a strong noise background, it is essential for mechanical fault diagnosis to extract a weak characteristic signal with a very small signal-to-noise ratio from a noisy interference. Therefore, this paper proposes a new method for diagnosing weak faults in asymmetric potential stochastic resonance. Compared with the existing methods, the asymmetric potential stochastic resonance method not only has characteristics common to the symmetric potential stochastic resonance, but can also change the inclination of the barrier and slope of the wall to obtain a better model structure. The proposed method solves the local adjustment problem of the existing method from the perspective of potential structure and optimizes the asymmetric system shape to better target frequency detection during much interference from noise. After simulation, a bearing failure test, and rolling mill gearbox bearing failure experiments, we concluded that the asymmetric potential stochastic resonance detection technology can effectively identify faults. Compared with the symmetric potential stochastic resonance method, the proposed method has better recognition.

An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Bingbing Hu ◽  
Chang Guo ◽  
Jimei Wu ◽  
Jiahui Tang ◽  
Jialing Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Resonance Method ◽  
Rolling Bearing ◽  
Grey Wolf Optimizer ◽  
Engineering Practice ◽  
Model Parameters ◽  
Grey Wolf ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

As a weak signal processing method that utilizes noise enhanced fault signals, stochastic resonance (SR) is widely used in mechanical fault diagnosis. However, the classic bistable SR has a problem with output saturation, which affects its ability to enhance fault characteristics. Moreover, it is difficult to implement SR when the fault frequency is not clear, which limits its application in engineering practice. To solve these problems, this paper proposed an adaptive periodical stochastic resonance (APSR) method based on the grey wolf optimizer (GWO) algorithm for rolling bearing fault diagnosis. The periodical stochastic resonance (PSR) model can independently adjust the system parameters and effectively avoid output saturation. The GWO algorithm is introduced to optimize the PSR model parameters to achieve adaptive detection of the input signal, and the output signal-to-noise ratio (SNR) is used as the objective function of the GWO algorithm. Simulated signals verify the validity of the proposed method. Furthermore, this method is applied to bearing fault diagnosis; experimental analysis demonstrates that the proposed method not only obtains a larger output SNR but also requires less time for the optimization process. The diagnosis results show that the proposed method can effectively enhance the weak fault signal and has strong practical values in engineering.

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