Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Dan Yang,Cancan Yi,Zhigang Wang,Zengbing Xu,Changming Liu,Hailin Mu

doi:10.1155/2017/2641546

Abstract

This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu’s norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

Highlights

The fault diagnosis is increasingly intelligent because of wide applications capability of artificial neural networks (ANNs)
This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique
This study proposes a novel competitive network—an adaptive resonance theory based on soft competition (SYART), whose topological structure is the same as Yu’s norm based ART

Summary

Introduction

The fault diagnosis is increasingly intelligent because of wide applications capability of artificial neural networks (ANNs). If a new fault category occurs, the training data sample of the data set used to train networks need be added, and these networks are required to retrain and learn the knowledge using the complete data sets This can result in a time-consuming and costly process [1]. In the real world, nobody knows what will happen time, so it is impossible to obtain the complete training data set which contain all data samples of all fault categories These characteristics limit the application of these neural networks in fault diagnosis field. As a solution to this problem, the adaptive resonance theory (ART) networks were developed and have been applied to the field of pattern recognition and fault diagnosis [4].

Review of ART and Yu’s Norm Based ART

ART Based on Soft Competition and Yu’s Norm

Neural Network Ensemble Based on Majority Voting

Diagnosis System Using Soft Competition Yu’s Norm ART

Findings

Conclusions