%0 Journal Article
%T 基于故障树的锂电池模组生产线故障诊断专家系统研究<br> Research on Fault Diagnosis Expert System of Lithium Battery Module Production Line Based on Fault Tree
%A 金明月
%A 陶为戈
%J Journal of Sensor Technology and Application
%P 201-211
%@ 2331-0243
%D 2023
%I Hans Publishing
%R 10.12677/JSTA.2023.112022
%X 针对现代生产线故障排查困难、维修成本高及维修人员不足等问题，提出了一种基于故障树的锂电池模组生产线故障诊断专家系统，对生产线进行全线智能诊断。首先，建立激光清洁工位故障树，对故障进行定性定量分析；其次，确定系统整体框架结构，并在此基础上进一步分析诊断专家系统知识库和推理机构建方法；然后，采用C#语言设计并实现了锂电池模组生产线故障诊断系统；最后，采用工业相机故障案例检验了系统的可行性。实验结果表明：基于故障树和贝叶斯网络的锂电池模组生产线故障诊断专家系统能高效定位故障点，可以为维修人员快速查找检修目标提供可靠参考依据。<br />
Aiming at the problems of trouble shooting, high maintenance cost and insufficient maintenance personnel in modern production line, a fault diagnosis expert system for lithium bat-tery module production line based on fault tree has been proposed to carry out intelligent diagnosis for the whole production line. First of all, the fault tree of laser cleaning station has been established to conduct qualitative and quantitative analysis of the fault; secondly, the overall framework of the system has been determined, and on this basis, the knowledge base and reasoning mechanism con-struction method of the diagnosis expert system have been further analyzed; then, the fault di-ag-nosis system of lithium battery module production line has been designed and implemented in C# language; finally, the feasibility of the system has been verified by the industrial camera fault case. The experimental result shows that the fault diagnosis expert system of lithium battery mod-ule production line based on fault tree can locate the fault point efficiently and provide reliable ref-erence for maintenance personnel to quickly find the maintenance target.
%K 故障树，生产线，故障诊断，专家系统<br> Fault Tree
%K Production Line
%K Fault Diagnosis
%K Expert System
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=63516