Fault diagnosis of rolling element bearing based on a new noise-resistant time-frequency analysis method

Hongchao Wang,Fang Hao

doi:10.21595/jve.2018.19288

Abstract

When fault arises in the rolling element bearing, the time-domain waveform of fault vibration signal will take on the characteristic of cyclostationarity, and the spectral correlation (SC) or spectral correlation density (SCD) basing on second order cyclic statistic is an effective cyclostationarity signal processing method. However, when the fault signal is surrounded by strong background noise, the traditional signal processing methods such envelope demodulation analysis and SC would not work effectively. The paper improves the SC method and a new time-frequency analysis method naming improved spectral correlation (ISC) is proposed. The proposed method is much more noise-resistant than SC through the verification of simulation analysis results. Besides, it takes on modulation phenomenon usually when fault arises in the rolling element bearing and the aim of fault feature extraction is to extract the fault characteristic frequency only or cyclic modulation frequency and the modulated frequency or carrier frequency buried in the object vibration signal is neglected. So, the paper improves the ISC further and the improved ISC (IISC) is proposed. The IISC will extract the modulation frequency only and it has the advantages of much clearer expression effect and better extraction effect. The effectiveness and feasibility of the proposed method are verified through the three kinds of fault (inner race fault, outer race fault and rolling element fault) of rolling element bearing. Besides, the advantages of the proposed method over the other relative time-frequency analysis methods such as ensemble empirical mode decomposition (EEMD) and spectral kurtosis (SK) are also presented in the paper.

Highlights

As the common used part and one of the most critical component in the rotating machinery, the study on the effective fault feature extraction method of rolling element bearing prior to its complete failure occurring in the practical engineering application is very important and necessary
Basing on the property of cyclostationarity when fault arises in the rolling element bearing, the paper improves the spectrum correlation (SC) or spectrum correlation density (SCD) basing on second order cyclic statistic and proposes a new time-frequency analysis method naming improving spectrum correlation (ISC)
When fault arises in the inner race or outer race of the rolling element bearing, the impulsive characteristic of the two kinds of fault is relative and the improved spectral correlation (ISC) method could extract the fault feature successfully

Summary

Introduction

As the common used part and one of the most critical component in the rotating machinery, the study on the effective fault feature extraction method of rolling element bearing prior to its complete failure occurring in the practical engineering application is very important and necessary. Time-frequency analysis is an effective signal processing method and has been used widely in fault feature extraction of rotating machinery. In paper [1], the time-frequency methods such as wavelet packet decomposition and short time Fourier transform are used in selecting the most impulsive frequency bands and the effective fault feature vector is extracted. FAULT DIAGNOSIS OF ROLLING ELEMENT BEARING BASED ON A NEW NOISE-RESISTANT TIME-FREQUENCY ANALYSIS METHOD. A generalized stepwise demodulation transform was proposed in paper [8] to improve the energy concentration level problem existing in common used time-frequency analysis methods. Noise suppression and resolution improvement are the remaining problems for the time-frequency analysis method application in rolling bearing fault feature extraction. A noise-resistant time-frequency method is proposed and used in fault diagnosis of rolling element bearing.

The theory of SC and the improved methods

Simulation

Experiment

Comparison

Conclusions