摘要
我国重型液压机数字化水平不断提高,其各部件和子系统越来越复杂,传统的故障人工排查和全现场维修方式已经不能满足装备运行和维护需求。针对此问题,本文研究了重型液压机故障状态及其故障原因,给出了重型液压机故障诊断流程方法和故障树,提出了基于贝叶斯网络建模的故障诊断模型迭代学习方法。在此基础上,设计出了一套重型液压机故障诊断系统。用例测试结果表明,本文提出的方法对于重型锻造液压机的故障诊断时效性更强、稳定性更好,有效减少了大型装备的维护成本。
With the continuous improvement of digital level of heavy hydraulic press in China,its components and subsystems are becoming more and more complex,and the traditional manual troubleshooting method and full field maintenance have been unable to adapt to the equipment operation and maintenance requirements.Aiming at this problem,fault state and cause of the heavy hydraulic press are researched, the fault diagnosis flow method and the fault tree(FTA) of heavy hydraulic press are given and an iterative learning method for fault diagnosis model is propsed based on Bayesian network modeling.On that basis,a set of the fault diagnosis system of the hydraulic press is designed,.The use case testing results show that the method proposed in this paper is more efficient and stable for fault diagnosis of heavy hydraulic press,and effectively reduces the maintenance cost about large equipment personnel.
作者
赵华
计鑫
张胜
王成城
ZHAO Hua;JI Xin;ZHANG Sheng;WANG Chengcheng(Instrumentation Technology and Economy Institute,Beijing 100055,China;Tianjin Tianduan Hydraulic Press Co.,Ltd,Tianjin 300142,China;Machinery Technology Development Co.,Ltd,Beijing 100044,China)
出处
《自动化与仪器仪表》
2020年第9期79-83,共5页
Automation & Instrumentation
基金
2018年工信部智能制造综合标准化项目《重型锻造装备远程诊断与预测性维护标准研究与试验验证》资助。
关键词
液压机
故障树
故障诊断
hydraulic press
A fault tree
fault diagnosis
