Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis

Lai, Zhonghong; Leng, Yonggang

doi:10.1016/j.ymssp.2016.04.002

Cited by 96 publications

(40 citation statements)

References 32 publications

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“…Duffing system (3) and Langevin system (4) are two common used bistable systems. Previous research shows that Duffing system has better adaptability to signals with large noise intensity due to the tunable damping ratio [25]. In this subsection, PSO algorithm is used for both systems to obtain their optimal output SNR under three groups of the same signals, which are given by A � 0.1, f 0 � 0.01 Hz, D � 0.4, 2, 5, the sampling frequency f s � 5 Hz, and the number of sampling points N � 20000 in equations (3) and (4).…”

Section: Comparison Between Optimization Results Of Langevinmentioning

confidence: 99%

“…e tunable system parameters in bistable SR systems contain the potential function parameters and damping ratio (only for the underdamped systems). Furthermore, researchers found that, for the signals with inappropriate amplitude and large frequency, it is necessary to introduce amplitude-transformation coefficient and scale-transformation coefficient, respectively, to transform the amplitude and frequency of the characteristic signal to an appropriate range [25]. Combining the amplitude-transformation coefficient and scale-transformation coefficient, multiparameter-adjusting SR methods are further proposed and the multiparameters adjustment rules are fully studied theoretically [26,27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Comparison Between Optimization Results Of Langevinmentioning

confidence: 99%

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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show abstract

“…Because of its obvious advantages in weak signal detection, SR has become a hotspot in the field of signal processing in recent years [15,16]. Han et al [17] combined wavelet transform with stochastic resonance theory, which can detect several high frequency weak signals in strong noise background by adjusting the amplitude of wavelet multiscale decomposition.…”

Section: Introductionmentioning

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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“…Stochastic resonance (SR) establishes a phenomenon where the additive noise can enhance the performance of some certain nonlinear systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Benzi initially observed the SR effect in climate model several decades ago [1].…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, various SR effects as types of array SR are investigated, for instance, the Suprathreshold SR [4].The positive role of the array noise has been found in some complex networks, e.g. stochastic pooling networks [5], scalefree networks [6] and small-world networks [7].Recently, the SR in some physical system focuses on the noisy bistable system, such as the bistable fractrional-order system, asymmetric bistable system and fractional harmonic oscillator and so on [8][9][10]. The SR effects can be measured by signal-to-noise ratio, the fisher information [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with Second-Order

Ma¹,

Zhang²,

Pan³

et al. 2017

JRNAL

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The transmission of weak noisy signal by parallel array of dynamical saturating nonlinearities with secondorder is studied. Firstly, the numerical results demonstrate that the output SNR can be enhanced by parallel array of dynamical saturating nonlinearities with second-order by tuning the internal noise. Secondly, the SR effects can be optimized by the self-coupling coefficient of the dynamical nonlinearity. Then, the SR effects when the nonGaussian noise acts as the external noise are superior to that with external Gaussian noise.

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Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis

Cited by 96 publications

References 32 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

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

Stochastic Resonance in an Array of Dynamical Saturating Nonlinearity with Second-Order

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