A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

Abstract

To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

“…Then f 0 is the carrier frequency, and the interval between the sideband and the center frequency is the modulation frequency. The bispectrum of the energy finite signal, both discrete and stationary s(n), n = 0, ±1, ±2, • • • , can be computed under the sum of its absolute values of the k-order cumulants of its finite, which can be expressed as Equation (4) [33][34][35][36].…”

Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

“…To overcome this drawback, Wu and Huang proposed the ensemble empirical mode decomposition (EEMD) method, which is a noise-assisted data analysis method by adding finite white noise to the investigated signal [ 14 ]. Because of their ease of use and excellent performance for complex signals, EMD-based methods have been widely applied in fault diagnosis [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. For example, Lei et al applied the EEMD to the rub-impact fault diagnosis of a power generator and early rub-impact fault diagnosis of a heavy oil catalytic cracking machine [ 15 ].…”

An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis

“…Zhen et al proposed a method based on the weighted average ensemble empirical mode decomposition (WAEEMD) and the modulation signal bispectrum (MSB) for rolling element bearing fault diagnosis. This method first decomposed the non-stationary signals into stationary intrinsic mode functions (IMFs) and then used a weighted average method to reconstructed the signal [14]. However, most of these techniques need expert knowledge [15] and consume expensive human labor [16], [17].…”