An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis

Qiao, Zijian; Lei, Yaguo; Lin, Jing; Jia, Feng

doi:10.1016/j.ymssp.2016.08.030

Cited by 221 publications

(96 citation statements)

References 31 publications

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“…7(b)) for a given coupling strength. It should be emphasized that this observation is useful since it is instructive for mechanical engineers to approximate the optimal noise intensity by the critical noise intensity in their fault detection applications [Vania & Pennacchi, 2004;Leng et al, 2006;Qiao et al, 2017;Zhang et al, 2017;Ma et al, 2018]. The dependence of the unperturbed order parameter and the spectral amplification factor on the coupling strength for different noise intensities is shown in Figs.…”

Section: Stochastic Resonance and Bifurcationmentioning

confidence: 88%

“…A special characteristic of SR lies in that a suitable amount of noise can lead to a distinctive enhancement of a weak input component to a nonlinear system [Jung, 1993;Gammaitoni et al, 1998;Cherubini et al, 2017], similarly to the periodic recurrence of the warm and the cold climates. To date, SR has been widely applied to various engineering fields, ranging from signal processing and detection [Lee et al, 2003;Sun & Kwong, 2007;Fu et al, 2018], early fault diagnosis [Vania & Pennacchi, 2004;Leng et al, 2006;Qiao et al, 2017;Ma et al, 2018], energy harvesting [Harne & Wang, 2013], to image processing [Singh et al, 2017]. Fig.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Resonance and Bifurcation of Order Parameter in a Coupled System of Underdamped Duffing Oscillators

Liu

Kang

et al. 2019

Int. J. Bifurcation Chaos

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The long-term mean-field dynamics of coupled underdamped Duffing oscillators driven by an external periodic signal with Gaussian noise is investigated. A Boltzmann-type H-theorem is proved for the associated nonlinear Fokker-Planck equation to ensure that the system can always be relaxed to one of the stationary states as time is long enough. Based on a general framework of the linear response theory, the linear dynamical susceptibility of the system order parameter is explicitly deduced. With the spectral amplification factor as a quantifying index, calculation by the method of moments discloses that both mono-peak and double-peak resonance might appear, and that noise can greatly signify the peak of the resonance curve of the coupled underdamped system as compared with a single-element bistable system. Then, with the input signals taken from laboratory experiments, further observations show that the mean-field coupled stochastic resonance system can amplify the periodic input signal. Also, it reveals that for some driving frequencies, the optimal stochastic resonance parameter and the critical bifurcation parameter have a close relationship. Moreover, it is found that the damping coefficient can also give rise to nontrivial non-monotonic behaviors of the resonance curve, and the resultant resonant peak attains its maximal height if the noise intensity or the coupling strength takes the critical value. The new findings reveal the role of the order parameter in a coupled system of chaotic oscillators.

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Section: Stochastic Resonance and Bifurcationmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Stochastic Resonance and Bifurcation of Order Parameter in a Coupled System of Underdamped Duffing Oscillators

Liu

Kang

et al. 2019

Int. J. Bifurcation Chaos

View full text Add to dashboard Cite

show abstract

“…The same set of input signals using different parameters of the system for stochastic resonance processing will produce different effect [34]. Setting ( ) = sin(2 0 ), Figure 2 shows the response amplitude changes with SR system parameters when = 0.2, 0 = 0.1 Hz, and = 0.1.…”

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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“…In the context of loud noise, the traditional bearing fault analysis methods usually start with noise reduction [5][6][7][8][9][10]. Although these methods can reduce the noise, they also can weaken the effective characteristic signal [10][11][12][13][14][15][16][17][18].…”

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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An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis

Cited by 221 publications

References 31 publications

Stochastic Resonance and Bifurcation of Order Parameter in a Coupled System of Underdamped Duffing Oscillators

Stochastic Resonance and Bifurcation of Order Parameter in a Coupled System of Underdamped Duffing Oscillators

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

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

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