Stochastic resonance of coupled time-delayed system with fluctuation of mass and frequency and its application in bearing fault diagnosis

Zhang, Gang; Wang, Hui; Zhang, Tianqi

doi:10.1007/s11771-021-4817-4

Cited by 10 publications

(4 citation statements)

References 32 publications

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“…Shi et al [ 13 ] designed an efficient diagnosis system by integrating an expert system and a fuzzy neural network to establish a diagnosis rule library. Zhang et al [ 14 ] studied the stochastic resonance behavior of a coupled stochastic resonance system with time delay under the fluctuation of mass and frequency. Yan et al [ 15 ] proposed an auxiliary indicator-based fault diagnosis system, which overcame the drawback of manually setting the modal parameters in the original singular spectral decomposition.…”

Section: Related Workmentioning

confidence: 99%

CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things

Gui

Yang

et al. 2023

Sensors

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Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a Causal-Factors-Aware Attention Network, CaFANet, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%.

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Section: Related Workmentioning

confidence: 99%

CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things

Gui

Yang

et al. 2023

Sensors

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“…where x(t) is the output signal, γ is the damping factor; U(x) is the bistable potential function; S(t) is the periodic signal and the amplitude is A, the frequency is f m , the phase is ϕ; N(t) is the noise, and D is the noise intensity, ξ(t) is Gaussian white noise with zero-mean and unit-variance [13][14][15].…”

Section: Basic Theory 21 Sr Theory Analysismentioning

confidence: 99%

Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance

Yang

Zhou

et al. 2022

Sensors

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Aiming at the problems of early weak fault feature extraction of bearings in rotating machinery, an improved stochastic resonance (SR) is proposed combined with the advantage of SR to enhance weak characteristic signals with noise energy. Firstly, according to the characteristics of the large parameters of the actual fault signal, the amplitude transform coefficient and frequency transform coefficient are introduced to convert the large parameter signal into small parameter signal which can be processed by SR, and the relationship of second-order parameters are introduced. Secondly, a comprehensive evaluation index (CEI) consisted of power spectrum kurtosis, correlation coefficient, structural similarity, root mean square error, and approximate entropy, is constructed through BP neural network. Moreover, this CEI is adopted as fitness function to search the optimal damping coefficient and amplitude transform coefficient with adaptive weight particle swarm optimization (PSO). Finally, according to the improved optimal SR system, the weak fault feature can be extracted. The simulation and experiment verify the effectiveness of the proposed method compared with traditional second-order general scale transform adaptive SR.

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“…In a related study, Banerjee et al [44] introduced the synchronization phenomenon in coupled hyperchaotic electronic oscillators featuring time delays. Additionally, Zhang et al [45] investigated the SR phenomena exhibited in coupled systems with time delays while considering the impacts of mass and frequency fluctuations and further applied these findings to the diagnosis of bearing faults. Meanwhile, the influence of time delays on SR and discharge frequency oscillations within cortical neural networks is thoroughly analyzed in [46].…”

Section: Introductionmentioning

confidence: 99%

Time-Delay Effects on the Collective Resonant Behavior in Two Coupled Fractional Oscillators with Frequency Fluctuations

He,

Wang,

Lin

2024

Fractal Fract

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In this study, we propose coupled time-delayed fractional oscillators with dichotomous fluctuating frequencies and investigate the collective resonant behavior. Firstly, we obtain the condition of complete synchronization between the average behavior of the two oscillators. Subsequently, we derive the precise analytical expression of the output amplitude gain. Based on the analytical results, we observe the collective resonant behavior of the coupled time-delayed system and further study its dependence on various system parameters. The observed results underscore that the coupling strength, fractional order, and time delay play significant roles in controlling the collective resonant behavior by facilitating the occurrence and optimizing the intensity. Finally, numerical simulations are also conducted and verify the accuracy of the analytical results.

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Stochastic resonance of coupled time-delayed system with fluctuation of mass and frequency and its application in bearing fault diagnosis

Cited by 10 publications

References 32 publications

CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things

CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things

Weak Fault Feature Extraction Method Based on Improved Stochastic Resonance

Time-Delay Effects on the Collective Resonant Behavior in Two Coupled Fractional Oscillators with Frequency Fluctuations

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