测试技术学报2017，Vol.31Issue(4)：290-297,8.DOI:10.3969/j.issn.1671-7449.2017.04.003
基于多传感器的神经网络和D-S证据理论在故障诊断中的应用
Multi-Sensor Application in Fault Diagnosis Based on Neural Network and D-S Evidence Theory
摘要
Abstract
To improve the accuracy of rolling bearing fault diagnosis,this paper puts forward a multi-sensor fault diagnosis method based on neural network and D-S evidence theory,and test the validity of model with three sensors monitoring data.First,two acceleration sensors and a acoustic sensor are used to collect vibration signals and noise signals of rolling bearing.Then,by using Ensemble Empirical Mode Decomposition(EEMD) decompose the vibration signals of two acceleration sensors and get each Intrinsic Mode function(IMF) component,the energy characteristics of each IMF component was extracted as the input vector of thesubnet 1 and subnet 2 respectively;meanwhile using WP(wavelet packet) extract noise signals energy spectrum feature and the result was taken as the input parameters of the subnet3;Finally,The local diagnostic results of three sub-networks are normalized processing and obtained each independent evidence,applying weighted correction method adjuste the conflict evidences and obtain the final fault diagnostic results by using D-S evidence theory to fuse the information of each evidence.The experimental results show that the method can effectively enhance the accuracy and reduce the uncertainty in rolling bearing fault diagnosis.关键词
多传感器/D-S证据理论/滚动轴承/故障诊断/信息融合Key words
multi-sensor/D-S evidence theory/rolling bearing/fault diagnosis/information fusion分类
信息技术与安全科学引用本文复制引用
周国宪,伍星,刘韬..基于多传感器的神经网络和D-S证据理论在故障诊断中的应用[J].测试技术学报,2017,31(4):290-297,8.基金项目
国家自然科学基金资助项目(51465022) （51465022）
