A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis

Li, Jimeng; Zhang, Jinfeng; Li, Ming; Zhang, Yungang

doi:10.1016/j.ymssp.2018.05.004

Cited by 118 publications

(67 citation statements)

References 35 publications

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“…e performance of an SR-based weak-signal detection method is mainly determined by the form of its nonlinear systems. Research has shown that SR can appear in both monostable systems [19,20] and bistable systems [21][22][23]. e latter ones, which involve the classical overdamped Langevin system and underdamped Duffing system, have much better SR performance than the former systems.…”

Section: Introductionmentioning

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%

“…In order to further enhance the detection performance of adaptive SR methods and extend their application in fault diagnosis, it is necessary to take the multiparameters into account and propose corresponding adaptive multiparameter-adjusting SR method. Moreover, in most references [22,[32][33][34]36], the adaptive methods were adopted directly to diagnosis mechanical faults without the reliability of the optimization results being verified and the optimization parameters being discussed. Furthermore, the comparison between the adaptive fault diagnosis performances of Langevin system and Duffing system, which has not been studied yet, should be conducted that a better bistable system can be selected in further applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Zhang et al [52] studied the coupling bistable system and used it for fault signal detection. Li et al [53] further studied a novel adaptive SR method based on coupled bistable systems and its application in rolling bearing fault diagnosis. Although the output of SNR has been improved, its effect is not good.…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings

Yuan

Chen

2020

Applied Sciences

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In mechanical equipment, rolling bearings analyze and monitor their fault based on their vibration signals. Vibration signals obtained are usually weak because the machine works in a noisy background that makes it very difficult to extract its feature. To address this problem, a second-order coupled step-varying stochastic resonance (SCSSR) system is proposed. The system couples two second-order stochastic resonance (SR) systems into a multistable system, one of which is a controlled system and the other of which is a controlling system that uses the output of one system to adjust the output of the other system to enhance the weak signal. In this method, we apply the seeker optimization algorithm (SOA), which uses the output signal-to-noise ratio (SNR) as the estimating function and combines the twice-sampling technology to adaptively select the parameters of the coupled SR system to achieve feature enhancement and collection of the weak periodic signal. The simulation and real fault data of a bearing prove that this method has better results in detecting weak signals, and the system output SNR is higher than the traditional SR method.

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“…Then, some indicators are often used to determine sensitive modes, including correlation coefficient, kurtosis, energy, and entropy. Furthermore, FSK [20], cyclostationary analysis (CA) [21], stochastic resonance (SR) [22], and other signal processing methods have also received increasing attention.…”

Section: Introductionmentioning

confidence: 99%

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

Zhou

et al. 2019

Mathematical Problems in Engineering

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The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.

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A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis

Cited by 118 publications

References 35 publications

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

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

A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings

A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

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