Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

Abstract

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method

“…rough using a nonlinear system, part of the noise energy is transferred to the low-frequency signal, and the resonance of the weak signal submerged in the noise is strengthened so as to reduce the noise interference and extract the signal features effectively [30][31][32]. In order to solve the feature extraction and fault diagnosis of rolling bearings under a large amount of noise, Li and Shi [33] proposed a signal processing method that combined EEMD and adaptive stochastic resonance and applied it to the rolling bearing vibration signal, effectively enhancing the weak fault features and extracting them. Considering that the weak fault characteristics of mechanical equipment are usually difficult to be extracted from strong noise, the new SR method that utilized the classical potential energy is applied to the fault vibration signal of rotating machinery, achieving an effective fault feature extraction for the analog signal with heavy noise [34].…”

A Periodic Potential Underdamped Stochastic Resonance Method and Its Application for Gear Fault Diagnosis

“…Liu et al proposed an adaptive SR detection method based on an improved artificial fish swarm algorithm, the numerical simulation and bearing experiments show that this method can effectively extract weak fault signals [32]. Then, Li et al proposed a multicomponent population average SR of singular value decomposition and ensemble empirical mode decomposition; the effective components were selected to be SR one by one, and then the overall average reached the goal of extracting weak signals [33], [34]. Qiao et al proposed a piecewise linear SR weak fault diagnosis method, which solved the output saturation characteristics of bistable SR by establishing a piecewise potential model [35].…”

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

“…Lu et al [24] proposed a sequence algorithm based on a multiscale noise-tuned stochastic resonance method to achieve signal demodulation, multiscale noise tuning, and bistable stochastic resonance sequences. Li and Shi [25] proposed a fault diagnosis method for rolling bearings based on strong background noise. is method not only overcomes the difficulty of selecting sensitive intrinsic mode function but also enhances the weak fault feature by combining it with adaptive stochastic resonance.…”