Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

Li, Jimeng; Li, Ming; Zhang, Jinfeng

doi:10.1016/j.jsv.2017.04.036

Cited by 117 publications

(65 citation statements)

References 27 publications

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“…However, some scholars have studied the delay stochastic resonance. For example, Li et al [30] proposed a weak signal detection method based on a time-delayed feedback monostable stochastic resonance system and adaptive minimum entropy deconvolution; this method achieves resonance detection of weak signals by selecting an appropriate time delay, feedback strength, and rescale ratio combined with a genetic algorithm. Lu et al [31] proposed a nonstationary weak signal detection method based on a time-delayed feedback stochastic resonance model.…”

Section: Introductionmentioning

confidence: 99%

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

Han

Wang

et al. 2019

Shock and Vibration

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Pulses caused by rotating mechanical faults are weak and often submerged in strong background noise, which can affect the accuracy of fault detection. To solve this problem, we study the stochastic resonance phenomenon of a tristable potential system based on strong noise background and also investigate the influence of time-delayed feedback on this stochastic resonance model. The effects of time-delayed feedback strength on potential energy, steady-state probability density function, and signal-to-noise ratio (SNR) are discussed. The results show that stochastic resonance can be enhanced or suppressed by adjusting the delay time and feedback strength. Combined with bearing fault diagnosis simulation research and experimental verification evaluation, the proposed time-delayed feedback tristable stochastic resonance fault diagnosis method is more effective than the classical stochastic resonance method.

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Section: Introductionmentioning

confidence: 99%

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

Han

Wang

et al. 2019

Shock and Vibration

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“…Sawalhi [21] and others applied it to fault diagnosis of rolling bearings. Li et al [22] proposed a method of combining monostable stochastic resonance with minimum entropy deconvolution based on time-delay feedback for fault diagnosis of rolling bearings. Liu et al [23] used the blind deconvolution method to filter the noise components of infrared spectroscopy and calculated the entropy value to determine the effect of noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

Guo

Han

et al. 2019

Entropy

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Minimum entropy deconvolution (MED) is not effective in extracting fault features in strong noise environments, which can easily lead to misdiagnosis. Moreover, the noise reduction effect of MED is affected by the size of the filter. In the face of different vibration signals, the size of the filter is not adaptive. In order to improve the efficiency of MED fault feature extraction, this paper proposes a firefly optimization algorithm (FA) to improve the MED fault diagnosis method. Firstly, the original vibration signal is stratified by white noise-assisted singular spectral decomposition (SSD), and the stratified signal components are divided into residual signal components and noisy signal components by a detrended fluctuation analysis (DFA) algorithm. Then, the noisy components are preprocessed by an autoregressive (AR) model. Secondly, the envelope spectral entropy is proposed as the fitness function of the FA algorithm, and the filter size of MED is optimized by the FA algorithm. Finally, the preprocessed signal is denoised and the pulse enhanced with the proposed adaptive MED. The new method is validated by simulation experiments and practical engineering cases. The application results show that this method improves the shortcomings of MED and can extract fault features more effectively than the traditional MED method.

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“…Bearing is the important component of mechanical equipment, and the reliable fault diagnosis method of bearing is key to ensuring its safe operation, which is helpful to safe operation of mechanical equipment [1][2][3][4][5][6][7][8][9]. Support vector machine (SVM) classifier [10][11][12] has a good ability to solve the classification problems, which has been applied in fault diagnosis of bearing [13].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Bearing by Utilizing LWT‐SPSR‐SVD‐Based RVM with Binary Gravitational Search Algorithm

Fei

2018

Shock and Vibration

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The fault diagnosis method of bearing based on lifting wavelet transform (LWT)-self-adaptive phase space reconstruction (SPSR)-singular value decomposition (SVD)-based relevance vector machine (RVM) with binary gravitational search algorithm (BGSA) is presented in this study, among which LWT-SPSR-SVD (LSS) is presented for feature extraction of the bearing vibration signal, the dynamic characteristics of lifting wavelet coefficients' (LWCs') reconstructed signals of the bearing vibration signal can be reflected by SPSR for LWCs' reconstructed signals of the bearing vibration signal, and BGSA is used to select the embedding space dimension and time delay of phase space reconstruction (PSR) and kernel parameter of RVM. In order to show the superiority of LWT-SPSR-SVD-based RVM with BGSA (LSS-BGSA-RVM), the traditional RVM trained by the training samples with the features based on LWT-SVD (LS-RVM) is used to compare with the proposed LSS-BGSA-RVM method. The experimental result demonstrates that compared with LS-RVM, LSS-BGSA-RVM can achieve the higher diagnosis accuracy for bearing.

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Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

Cited by 117 publications

References 27 publications

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application

Application of a Novel Adaptive Med Fault Diagnosis Method in Gearboxes

Fault Diagnosis of Bearing by Utilizing LWT‐SPSR‐SVD‐Based RVM with Binary Gravitational Search Algorithm

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