Intelligent Fault Identification for Rolling Element Bearings in Impulsive Noise Environments Based on Cyclic Correntropy Spectra and LSSVM

Xuejun Zhao,Limin Jia,Yong Qin,Changbo He

doi:10.1109/access.2020.2976868

Abstract

Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures. However, impulsive background noise in industrial fields also presents a similar fault-excited characteristic, which brings interference to the fault diagnosis of rolling element bearings. Focusing on this issue, this paper proposes a new feature extraction method based on the cyclic correntropy spectrum (CCES) for intelligent fault identification.. In this study, the cyclic correntropy (CCE) function is introduced to suppress the impulsive noise. A simplified frequency spectrum named CCES is obtained for the feature extraction. Then, narrowband kurtosis vectors are extracted from the CCES. Finally, these extracted features are used to train the least squares support vector machine (LSSVM) for the fault pattern identification. Analyses of two bearing datasets, including train axle bearing data that are contaminated by impulsive noise are used as case studies for the validation of the proposed method. To illustrate the advancement of the new method, performance comparisons with two recently developed methods are conducted. The experimental results verify that the proposed method not only outperforms these two methods but also exhibits a stable self-adaptation ability.

Highlights

Rolling element bearings are one of the mostly used components in the mechanical industry, such as railway rolling stocks, machine tools and internal combustion engines
This paper proposes an intelligent rolling element bearing fault identification method based on the cyclic correntropy spectrum (CCES) and least squares support vector machine (LSSVM), providing a reliable diagnosis tool for bearings and even other rotating machinery in the presence of impulsive noise
Considering the characteristics of three fault patterns including the inner race fault, outer race fault, and rolling element fault, three frequency domain kurtosis (FDK) indexes are extracted from the cyclic frequency domain projection of the CCES

Summary

INTRODUCTION

Rolling element bearings are one of the mostly used components in the mechanical industry, such as railway rolling stocks, machine tools and internal combustion engines. Combining the cyclostationary modeling and correntropy theory, this technique performed well when processing the communication signals in the presence of impulsive noise This method has rarely been tried on vibration signal analysis and its applicability needs further reinforcement. This paper proposes a fault feature extraction method based on the CCES to process bearing fault signals in the presence of impulsive noise. This paper proposes an intelligent rolling element bearing fault identification method based on the CCES and LSSVM, providing a reliable diagnosis tool for bearings and even other rotating machinery in the presence of impulsive noise. The superiority of the Gaussian kernel function can be summarized into two aspects: extracting higher statistical moments and transforming values of outliers into zero These two properties enable the correntropy function to process signals interfered with by non-Gaussian noise, especially impulsive noise. The correntropy function has been widely applied in the fields of nonlinearity tests [42], pattern recognition [43], and so on

CYCLIC SPECTRAL ANALYSIS

NARROWBAND FEATURE EXTRACTION BASED ON CYCLIC CORRENTROPY SPECTRUM

LEAST SQUARES SUPPORT VECTOR MACHINE

FAULT PATTERN IDENTIFICATION BASED ON THE CCES FEATURE AND LSSVM

Findings

CONCLUSION