基于模糊神经网络的MVB故障诊断算法  

Fault Diagnosis Algorithm for MVB Based on Fuzzy Neural Network

作　　者：吕洪武[1] 赵航 王宏志 胡黄水[1] Lu Hongwu;ZHAO Hang;WANG Hongzhi;HU Huangshui(College of Computer Science and Engineering,Changchun University of Technology,Changchun 130012,China;College of Computer Science and Engineer ing,Jilin University of Architecture and Technology,Changchun 130114,China)

机构地区：[1]长春工业大学计算机科学与工程学院,长春130012 [2]吉林建筑科技学院计算机科学与工程学院,长春130114

出　　处：《吉林大学学报（理学版）》2020年第1期104-108,共5页Journal of Jilin University:Science Edition

基　　金：吉林省教育厅“十三五”科学技术研究项目（批准号：JJKH20181042KJ;JJKH20181013KJ）;吉林省发改委产业技术研究与开发项目（批准号：2018C039-2）

摘　　要：针对多功能车辆总线具有随机性和不确定性导致故障诊断准确率较低的问题,设计一种基于模糊神经网络的MVB故障诊断算法.首先根据MVB故障类型给出诊断模型,然后采用减法聚类生成数量较少的模糊规则,最后采用T-S模糊神经网络对故障进行分类.在MATLAB环境下对该算法的拟合能力及诊断准确率进行仿真分析的结果表明,该算法简化了模糊神经网络结构,有效提高了故障诊断准确率.Aiming at the problem of low fault diagnosis accur acy caused by the randomness and uncertainty of multifunction vehicle bus(MVB),we designed a fault diagnosi s algorithm for MVB based on fuzzy neural network.Firstly,the diagnosis model was given according to the fault types of MVB.Secondly,the subtraction clustering was used to generate fewer fuzzy rules.Finally,the T-S fuzzy neural network was used to classify the faults.The simulation results of the fitting ability and diagnosis accuracy of the algorithm in MATLAB show that the proposed algorithm simplifies the structure of the fuzzy neural network and effectively improves the accuracy of fault diagnosis.

关 键 词：多功能车辆总线 故障诊断 模糊神经网络 减法聚类 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]