A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

Hunglinh Ao,Junsheng Cheng,Tung Khac Truong,Kenli Li

doi:10.1155/2014/825825

Abstract

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

Highlights

Roller bearings are important and frequently encountered components in rotating machines, which are found in widespread industrial applications
The local characteristic-scale decomposition (LCD) method is developed from the simple assumptions that any complicated signal consists of several intrinsic scale components (ISCs) and any two ISCs are independent of each other
A roller bearing fault diagnosis method based on LCD energy entropy and the Artificial Chemical Reaction Optimisation Algorithm (ACROA)-Support Vector Machine (SVM) was investigated in this paper

Summary

Introduction

Roller bearings are important and frequently encountered components in rotating machines, which are found in widespread industrial applications. Fault diagnosis includes two aspects: feature extraction and pattern recognition. When a fault occurs in a roller bearing, it is very difficult to extract the fault characteristic information from the nonstationary vibration signals [1, 2]. The traditional diagnosis techniques extract the fault characteristic information from the waveforms of the vibration signals in either the time domain or the frequency domain. Criterion functions are constructed to identify the condition of the roller bearing. It is very difficult to accurately evaluate the condition of a roller bearing through an analysis in the time or frequency domain only [2, 3]

Methods

Results

Conclusion