基于FTA和FNN的液压系统故障诊断方法研究  

A Hydraulic System Fault Diagnosis Method Based on FTA and FNN

作　　者：游张平[1] 叶晓平[1] 朱银法[1] 胡笑奇[1] 

机构地区：[1]丽水学院机械工程学系,丽水323000

出　　处：《机械科学与技术》2013年第12期1855-1858,共4页Mechanical Science and Technology for Aerospace Engineering

基　　金：国家科技支撑计划项目(2013BAC16B02);浙江省自然科学基金重点项目(LZ12F02001);丽水市公益性技术应用项目(2012JYZB34);机械设计及理论浙江省重中之重学科;浙江理工大学重点实验室基金项目(ZSTUMD2011A005)资助

摘　　要：针对液压系统故障的复杂性和不确定性等特点,传统的故障推理方法难以满足液压系统故障诊断的要求,提出了基于故障树分析和专家经验知识的模糊神经网络故障诊断方法。以起重设备液压系统为研究对象,建立故障树模型,基于故障树信息和专家经验知识,建立模糊神经网络诊断模型及并提取训练数据,在此基础上,运用统计参数法确定模糊预处理所需的模糊隶属函数。将训练好的网络模型应用于实例诊断,实验结果验证了该方法的实用性和有效性。The hydraulic system fault diagnosis method based on fuzzy neural networks （FNN）, fault tree analysis （FTA） and expertise knowledge is proposed to overcome the shortcomings of the traditional fault diagnosis methods because of the complexity coupling and uncertainty of hydraulic system fault. Taking lifting equipment＇s hydraulic system as study object, a fault tree model is set up, and the fault tree information and expertise knowledge are em- ployed to establish the FNN structure and extract training data. The fuzzy membership functions, which are needed in fuzzy pre-processing, are confirmed by using the statistical parameter method. Finally, the trained FNN model is applied to examples; the application results show that the hydraulic system fault diagnosis method based on FTA and FNN is effective and practicable.

关 键 词：液压系统 故障诊断 故障树分析 神经网络 

分 类 号：TH137[机械工程—机械制造及自动化]