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

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
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.

scholarly journals Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Z. H. Lai ◽  
S. B. Wang ◽  
G. Q. Zhang ◽  
C. L. Zhang ◽  
J. W. Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Transformation Coefficient ◽  
System Parameters ◽  
Duffing System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Adaptively Adjusting

The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

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.

Application Research on Entropic Stochastic Resonance for Bearing Fault Diagnosis

2013 ◽  
Vol 718-720 ◽  
pp. 1195-1200 ◽  
Author(s):  
Huo Rong Ren ◽  
Ya Nan Ma ◽  
Xiao Wang ◽  
Sheng Gang Li
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Driving Force ◽  
Ambient Noise ◽  
Good Effect ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Application Research ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

The Entropic stochastic resonance (ESR) is the appearance of stochastic resonance (SR) when the dynamics of aBrownian particle takes place in a confined medium. The presence of unevenboundaries, giving rise to anentropic contribution to the potential, may upon application of a periodic driving force result in anincrease of thespectral amplification at an optimum value of the ambient noise level.The ESR provides a way for weak signal detection, and this paper applies it to bearing fault diagnosis. Preliminary numerical simulation andexperimental result of practical bearing test signal show that the ESRhas a good effect on weak feature extraction and bearing fault detection.

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.

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