Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

Abstract

Abstract:Bearing vibration response studies are crucial for the condition monitoring of bearings and the quality inspection of rotating machinery systems. However, it is still very difficult to diagnose bearing faults, especially rolling element faults, due to the complex, high-dimensional and nonlinear characteristics of vibration signals as well as the strong background noise. A novel nonlinear analysis method-the symplectic entropy (SymEn) measure-is proposed to analyze the measured signals for fault monitoring of rolling bearings. The core technique of the SymEn approach is the entropy analysis based on the symplectic principal components. The dynamical characteristics of the rolling bearing data are analyzed using the SymEn method. Unlike other techniques consisting of high-dimensional features in the time-domain, frequency-domain and the empirical mode decomposition (EMD)/wavelet-domain, the SymEn approach constructs low-dimensional (i.e., two-dimensional) features based on the SymEn estimate. The vibration signals from our experiments and the Case Western Reserve University Bearing Data Center are applied to verify the effectiveness of the proposed method. Meanwhile, it is found that faulty bearings have a great influence on the other normal bearings. To sum up, the results indicate that the proposed method can be used to detect rolling bearing faults.

Induction motor fault classification via entropy and column correlation features of 2D represented vibration data

“…According to the probability of each state, the entropy of each of the wavelet sub-images can be calculated as in (19):…”

“…Immovilli, Bellini, Rubini and Tassoni also compared the vibration-based method with the current-based method, and concluded that although the current-based method is suitable only for low-frequency working conditions, the vibration-based method is suitable for both low-and high-frequency working conditions [17]. Lei and Meng proposed the Symplectic Entropy method with Radial Basis Function (RBF) classifier for vibration-based defect classification for four conditions: normal condition, outer race defect, rolling element defect and inner race defect, and achieved 99.8% test accuracy with 6,000 vibration samples [19]. In more recent research, Li, Wang, Si and Huang applied an entropy-based defect classification method to the same data used in our research, and achieved 98.75% accuracy for ball defect detection and 100% accuracy for inner and outer race defect detection [20].…”