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

Zhou, Peng; Lu, Siliang; Liu, Fang; Liu, Yongbin; Liu, Guihua; Zhao, Jiwen

doi:10.1016/j.jsv.2016.12.017

Cited by 63 publications

(30 citation statements)

References 33 publications

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“…For instance, Wang et al proposed an adaptive index named weighted power spectrum kurtosis for detecting the fault characteristic frequency of the rolling element bearing [45]. Zhou et al proposed a synthetic index method for extracting the fault characteristic frequency of the rolling element bearing adaptively [46]. In this regard, to make this method more practical, some adaptive indexes could be introduced in the proposed SVVR method, and adaptive VR will be a further study in the future.…”

Section: Discussion Some Discussion Are Listed As Followsmentioning

confidence: 99%

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Liu

Dai

et al. 2017

Shock and Vibration

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Bearing is a key part of rotary machines, and its working condition is critical in normal operation of rotary machines. Vibrational signals are usually analyzed to monitor the status of bearing. However, information on the status of bearing is always buried in heavy background noise; that is, status information of bearing is weaker than the background noise. Extracting the status features of bearing from signals buried in noise is difficult. Given this, a step-varying vibrational resonance (SVVR) method based on Duffing oscillator nonlinear system is proposed to enhance the weak status feature of bearing by tuning different parameters. Extraction ability of SVVR was verified by analyzing simulation signal and practical bearing signal. Experimental results show that SVVR is more effective in extracting weak characteristic information than other methods, including multiscale noise tuning stochastic resonance (SR), Woods-Saxon potential-based SR, and joint Woods-Saxon and Gaussian potential-based SR. Two evaluation indices are investigated to qualitatively and quantitatively assess the fault detection capability of the SVVR method. The results show that the SVVR can effectively identify the weak status information of bearing.

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Section: Discussion Some Discussion Are Listed As Followsmentioning

confidence: 99%

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Liu

Dai

et al. 2017

Shock and Vibration

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“…Bistable SR systems have two typical forms. One is described by equation (1) indicating an underdamped Duffing system [31] driven by a weak signal s(t) and a source of noise n(t), and the other is described by equation (2) indicating an overdamped Langevin system [37] whose inertia term is ignored. In both bistable systems, dU(x)/dx � − ax + bx 3 denotes the force of the potential field, whose potential function U(x) � − ax 2 /2 + bx 4 /4 with a and b being positive system parameters.…”

Section: Multiparameter-adjusting Sr For Bistable Systemsmentioning

confidence: 99%

“…Moreover, the manual adjustment method can only be applied in offline detections. In order to solve these issues, by using some multiparameter optimization algorithms such as particle swarm optimization (PSO) [28] and genetic algorithm (GA) [22,29], some adaptive parameter-adjusting methods, which can realize SR in online conditions with high efficiency and high reliability, have been proposed and studied [30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

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

Lai

Wang

Zhang

et al. 2020

Shock and Vibration

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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.

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“…In the field of signal processing, stochastic resonance can transfer the energy of some noise signals to useful signals, which is beneficial to the identification of weak signals submerged in noise [30][31][32]. The noise signal is input to the bistable or multistable nonlinear system.…”

Section: Stochastic Resonancementioning

confidence: 99%

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

Chen

2017

International Journal of Rotating Machinery

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Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

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

Cited by 63 publications

References 33 publications

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

Enhanced Bearing Fault Detection Using Step-Varying Vibrational Resonance Based on Duffing Oscillator Nonlinear System

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

Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

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