摘要
针对矿井提升机制动系统故障样本少难以准确诊断,提出支持向量机(SVM)的故障诊断方法。为了解决支持向量机参数选择困难和其对于故障诊断的影响,提出利用粒子群优化算法(PSO)对支持向量机的参数进行优化,提高提升机故障诊断分类的准确率。利用组态王进行数据的采集,并且将采集的数据通过数据库传输到网页监测画面实现远程监测。实验结果显示,该故障诊断方法的故障分类准确率很高,响应速度快,并且可以实现网页监控画面和故障诊断所需数据实现共享。
For the fault samples of mine hoist braking system are few and the hoist fault is difficult to be accurately diagnosed,a fault diagnosis method based on the support vector machine is proposed.In order to solve the difficulty of parameter selection of support vector machine and The influence of parameters of SVM on fault diagnosis,a method for parameter optimization of SVM based on particle swarm optimization algorithm（PSO）is proposed,the accuracy of fault diagnosis and classification of hoist is improved.The data collected by the Kingview,the data is transmitted to the web page through the data base and realize the remote monitoring.the experimental results show that the accuracy of fault classification is very high by using the fault diagnosis method,and the response speed is fast,and It can realize the sharing of the data between the web monitor screen and the fault diagnosis.
作者
卢亚洲
王学文
杨兆建
高玉光
LU Ya-zhou;WANG Xue-wen;YANG Zhao-jian;GAO Yu-guang(College of Mechanical Engineering, Taiyuan University of Technology, Shanxi Taiyuan 030024, China;Key Laboratory of Fully Mechanized Coal Mining Equipment of Shanxi Province, Shanxi Taiyuan 030024, China)
出处
《机械设计与制造》
北大核心
2018年第6期239-242,共4页
Machinery Design & Manufacture
基金
国家自然科学基金项目资助(51475318)
