Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system

Liu, Xiaole; Liu, Houguang; Yang, Jianhua; Litak, Grzegorz; Cheng, Gang; Han, Songtao

doi:10.1016/j.ymssp.2017.04.006

Cited by 84 publications

(41 citation statements)

References 42 publications

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“…In the above formula, [N/s] means the integral of N/s; generally, the scale factor is s ∈ [1,12]. Obviously, when s = 1, the coarsening sequence is the original sequence.…”

Section: Multi-scale Permutation Entropymentioning

confidence: 99%

“…Therefore, the features of a weak fault are usually difficult to extract, and it is very important to select an appropriate feature extraction method that can extract the information closely related to the fault (namely fault-sensitive information) [3]. In recent years, scholars have proposed some effective methods for the approximation of weak faults, including time domain feature analysis [4], frequency domain feature analysis [5], time-frequency domain feature analysis [6], entropy feature analysis [7,8], correlation detection [9], decomposition algorithm [10,11], stochastic resonance theory [12,13], enhancement algorithm [14,15], combination of filters bank technique and dynamic cumulative sum (DCS) [16], and intelligent algorithm [17]. The sealing characteristic of the hydraulic system structure results in hydraulic faults being concealed and easily affected by random factors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Huang

Wang

et al. 2019

Entropy

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With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

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“…In the above formula, [N/s] means the integral of N/s; generally, the scale factor is s ∈ [1,12]. Obviously, when s = 1, the coarsening sequence is the original sequence.…”

Section: Multi-scale Permutation Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Huang

Wang

et al. 2019

Entropy

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show abstract

“…where λ and δ denote the wavelet coefficients and the given threshold, respectively. f(λ) is the wavelet coefficient after applying the threshold [22].…”

Section: Wavelet Threshold Denoisingmentioning

confidence: 99%

Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

Xiao

Wen

Cunyi

et al. 2018

Shock and Vibration

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Rolling bearings are the core components of the machine. In order to save costs and prevent accidents caused by bearing failures, the rolling bearing fault diagnosis technology has been widely used in the industrial field. At present, the proposed methods include wavelet transform, morphological filtering, empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD), which have obvious shortcomings. As it is difficult to extract the fault characteristic frequency caused by nonlinear and nonstationary features of the rolling bearing fault signal, this paper presents a fault feature extraction method of rolling bearing based on nonlinear mode decomposition (NMD) and wavelet threshold denoised method. First of all, the fault signal was preprocessed via wavelet threshold denoising. Then, the denoised signal was decomposed by using NMD. Next, the mode component envelope spectrum was made. Finally, the fault characteristic frequency of rolling bearing was extracted. The method was compared with EMD through the simulation experiment and rolling bearing fault experiment. Meanwhile, two indicators including signal-noise ratio (SNR) and root-mean-square error (RMSE) were also established to evaluate the fault diagnosis ability of this method, and the results show that this method can extract the fault characteristic frequency accurately.

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“…In multistable potentials, Li [17] presented overdamped multistable SR with a tristable potential to extract the fault characteristics of the rolling mill gearbox and the detection results show that the multistable SR method has better processing effects than the classical bistable SR method for weak signals. Han [18] proposed a multi-stable SR system for multi-frequency weak signal detection and Liu [19] improved an adaptive SR with the periodic potential system using a trigonometric function. In short, SR with multistable potential has a better filtering performance than bistable SR.…”

Section: Introductionmentioning

confidence: 99%

A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Yuan

Wang

et al. 2019

Symmetry

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Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

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Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system

Cited by 84 publications

References 42 publications

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Research on Fault Feature Extraction Method of Rolling Bearing Based on NMD and Wavelet Threshold Denoising

A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

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