Stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian noise and additive white noise

Guo, Yongfeng; Yajun, Shen; Tan, Jianguo

doi:10.1016/j.cnsns.2016.02.032

Cited by 43 publications

(12 citation statements)

References 32 publications

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“…Generally, the Gaussian noise is universal in nature and engineering, and has been extensively investigated in fault diagnosis and processing, while the non-Gaussian noise has not obtained enough attention up to now [20][21]. In fact, the non-Gaussian noise cannot be neglected as it plays an important role in the effect on the fault diagnosis results.…”

Section: Introductionmentioning

confidence: 99%

An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

et al. 2020

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Many methods have been developed for machinery fault diagnosis addressing only Gaussian noise reduction, the major weaknesses of these methods are their performance for non-Gaussian noise suppression. Modulation signal bispectrum (MSB) is a useful signal processing method with significant advantages over traditional spectral analysis as it has the unique properties of Gaussian noise elimination and demodulation. However, it is susceptible to non-Gaussian noise that normally occurs in the practical applications. In view of the deficiency of MSB, in this paper, an autoregressive (AR) modeling filter was developed based on non-Gaussian noise suppression to improve the performance of MSB for machinery fault detection. The AR model was considered as a pre-filter process unit to reduce the non-Gaussian noise. And the order of the AR model, which can affect the performance of the AR filter, was determined adaptively using a kurtosis-based indicator. However, the existing nonlinear modulation components remain in the AR filtered signal. The MSB was then applied to decompose the modulated components and eliminate the Gaussian noise from the filtered signal using AR model for the fault feature extraction accurately. The advantage of the AR model can effectively manage non-Gaussian noise, whereas the MSB can suppress Gaussian noise and is illustrated in the simulation signal. Furthermore, the proposed AR-MSB method was applied to analyze the vibration signals of defective bearings with outer race and inner race faults. By comparison, the proposed approach had a superior performance over conventional MSB and fast kurtogram in extracting fault features and was precise and effective for rolling element bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

et al. 2020

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“…Kong et al 36 examined the dynamic behaviors and the stability of the linear guide considering contact actions by a multi-term incremental harmonic balance method and formulated a generalized time-varying and piecewise nonlinear dynamic model of the linear guide to perform an accurate investigation of its dynamic behaviors and stability. Guo et al 37 and Xi et al 38 investigated the stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian and additive white noise, and discussed the effect of noises, periodic signal, and system parameters on signal-to-noise ratio. Wang et al 39 derived the relationship between contact deformation and force via Hertz contact theory and obtained piecewise nonlinear interaction forces.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al. 37 and Xi et al. 38 investigated the stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian and additive white noise, and discussed the effect of noises, periodic signal, and system parameters on signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Periodic response characteristics on a piecewise hysteresis nonlinear system

Wang

Chen

Liu

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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A piecewise hysteresis nonlinear dynamic model with nonlinear stiffness and damping is established for a system with alternating elastic constraints and hysteresis nonlinearity. First, the amplitude-frequency response equation of the system is solved by using the averaging method under periodic excitation. Two frequency regions with multiple solutions are then identified in the system, and the amplitude-frequency response characteristics under different system parameters are obtained. Thus, the influence law of different piecewise nonlinear factors on system stability is explored. Second, the bifurcation behavior of the system under different external disturbances is analyzed. Given the change of perturbation parameters, the system is found to be a complex dynamic system with alternating periodic, double periodic, and chaotic motions, and many other forms of movement. Moreover, lower buffer damping leads to the complex and unpredictable chaotic state of the system's dynamic behavior.

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“…This typically requires the values of the field variable and its normal derivatives to be known on the boundary. Since most of the solution methods [1][2][3][4][5][6][7] of such ill-posed problems are iterative, they require regularization.…”

Section: Introductionmentioning

confidence: 99%

Inverse Determination of Spatially Varying Heat Capacity and Thermal Conductivity in Arbitrary 2d Objects

Reddy¹,

Dulikravich²,

Zeidi³

2017

Proceedings of CHT-17 ICHMT International Symposium on Advances in Computational Heat Transfer May 28-June 1, 2017, Napoli, Ita

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A methodology for non-destructive simultaneous estimation of spatially varying thermal conductivity and heat capacity in 2D solid objects was developed that requires only boundary measurements of temperatures. The spatial distributions were determined by minimizing the normalized sum of the least-squares differences between measured and calculated values of the boundary temperatures. Computing time was significantly reduced for the entire inverse parameter identification process by utilizing a metamodel created by an analytical response surface supported by an affordable number of numerical solutions of the temperature fields obtained by the high fidelity finite element analyses. The minimization was performed using a combination of particle swarm optimization and the BFGS algorithm. The methodology has shown to accurately predict linear and nonlinear spatial distributions of thermal conductivity and heat capacity in arbitrarily shaped multiply-connected 2D objects even in situations with noisy measurement data thus proving that it is robust and accurate. The current drawback of this method is that it requires an a priori knowledge of the general spatial analytic variation of the physical properties. This can be remedied by representing such variations using products of infinite series such as Fourier or Chebyshev and determining correct values of their coefficients.

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Stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian noise and additive white noise

Cited by 43 publications

References 32 publications

An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

An enhanced modulation signal bispectrum analysis for bearing fault detection based on non-Gaussian noise suppression

Periodic response characteristics on a piecewise hysteresis nonlinear system

Inverse Determination of Spatially Varying Heat Capacity and Thermal Conductivity in Arbitrary 2d Objects

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