Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Abstract

Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal

“…Shi et al [53] used cascaded multi-stable stochastic resonance system as the pretreatment to remove noise, which could remove high-frequency noise step by step and improve energy of lowfrequency signal. In order to enhance the weak signal characteristics of low frequency, Cui et al [54] developed noise reduction pretreatment technology based on cascaded adaptive second-order tristable stochastic resonance. In order to suppress the noise in the vibration signal, Zheng et al [55] proposed spectral envelope-based adaptive empirical Fourier decomposition (SEAEFD) method to optimize the spectrum segmentation boundary so that the obtained frequency band contained the least noise components.…”

A Novel Piecewise Tri-Stable Stochastic Resonance System Driven by Dichotomous Noise

“…For many domains, SR can be used and has good performance [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. In the aspect of feature extraction and signal enhancement of mechanical signals, SR is used more and more because it is different from other denoising methods [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Han et al used colored noise to drive SR, studied the mean first-passage time of the system at this time, and discussed its relationship with the parameters [ 17 ].…”