An improved extreme-point symmetric mode decomposition method and its application to rolling bearing fault diagnosis

Ping Xia,Hua Xu,Mohan Lei,Zaichao Ma

doi:10.21595/jve.2018.19234

Abstract

HHT (Hilbert-Huang Transform) which consist of EMD (Empirical Mode Decomposition) and HT (Hilbert Transform) now is the most widely used time-frequency analysis technique for rolling element bearing fault diagnosis, however, its fault characteristic information extraction accuracy is usually limited due to the problem of mode mixing in EMD. ESMD (Extreme-point symmetric mode decomposition) is a novel development of HHT which is promising to alleviate this limitation and it has been applied to some fields successfully, but its application for rolling bearing fault diagnosis has rarely been seen in the literature. In this paper, ESMD is applied to extract the bearing fault characteristics for rolling bearing fault detection, and the results proved that ESMD can have a better fault diagnose effect than EMD and HT. What’s more, for further improving bearing fault characteristic extraction accuracy of rolling bearing vibration signals, the sifting scheme is proposed for selecting the sensitive fault-related IMFs (intrinsic mode functions) generated by ESMD, in which a weighted kurtosis index is introduced for automatic selection and reconstruction of the fault-related IMFs, and then the original and reconstructed bearing fault vibration signal after performing Hilbert transform as the results to diagnose the incipient rolling bearing fault. ESMD combined with the proposed sifting scheme are applied to diagnose the simulated and experimental signals, and the results confirmed that the sifting scheme based ESMD is superior to the other conventional method in rolling bearings fault diagnosis.

Highlights

Rolling element bearing is one of the most fundamental and vital rotating components in mechanical industry, and their failure is one of the main reasons of machinery malfunction [1, 2]
The fault characteristic of rolling bearing vibration signal is masked by the heavy noise and not evident enough (Fig. 12), while it is obvious that fault characteristic frequency 53 Hz and its harmonics of rolling bearing are very evident (Fig. 13), so incipient bearing fault can be detected
Comparisons between the novel development method of Hilbert-Huang transform (HHT)-ESMD and the conventional HHT are conducted with the simulated fault vibration signals of rolling bearing, and the results indicated that ESMD can help achieving better performance than the conventional HHT for rolling bearing fault diagnosis

Summary

Introduction

Rolling element bearing is one of the most fundamental and vital rotating components in mechanical industry, and their failure is one of the main reasons of machinery malfunction [1, 2]. Wang and their co-workers [12,13,14] indicated that the noise-assisted processing method (EEMD) may destroy the vibration signal, which means losing some intrinsic physical meaning of the raw bearing vibration signals, and lead to an inaccurate result They proposed ESMD (Extreme-point symmetric mode decomposition), which is a novel improvement method of HHT for the nonlinear and non-stationary signal processing [12, 13]. ESMD has such a suitable adaptive decomposition baseline and the components decomposed by the baseline of ESMD can preserve its intrinsic physical meaning of the raw vibration signal to a certain extent [12, 13] For this reason, fault characteristic components and other disturbance components of rolling bearing vibration signals can be separately decomposed.

Decomposition algorithm of ESMD

Shifting scheme-based ESMD for rolling bearing fault diagnosis

Simulated verification

Experimental demonstration

Conclusions