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.
Highlights
Rolling element bearings have been widely used in modern industries, but failure may lead to fatal breakdowns and costly downtime [1]
Considering all the above, a novel approach based on the weighted average ensemble empirical mode decomposition (WAEEMD) and the modulation signal bispectrum (MSB) for rolling element bearing fault diagnosis is proposed in this research work
A novel fault detection approach based on WAEEMD and MSB analysis was proposed
Summary
Rolling element bearings have been widely used in modern industries, but failure may lead to fatal breakdowns and costly downtime [1]. These methods are effective in selecting representative IMFs, but they do not take into account variations in impulse signal amplitude and instantaneous frequency, which may affect the accuracy of fault diagnosis To solve this issue, Deng et al [36] proposed using Teager energy kurtosis (TEK). The above methods focus on analyzing individual IMF to extract fault features without considering the useful fault information that is usually omitted in discarding IMFs. Considering all the above, a novel approach based on the weighted average ensemble empirical mode decomposition (WAEEMD) and the MSB for rolling element bearing fault diagnosis is proposed in this research work.
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