神经网络在提升机故障诊断中的应用  

Application of Neural Network to Fault Diagnosis of Hoist

作　　者：雷勇涛[1] 杨兆建[1] 

机构地区：[1]太原理工大学机械工程学院,山西太原030024

出　　处：《华南理工大学学报（自然科学版）》2010年第2期67-72,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：山西留学基金资助项目(2004-19);山西省基础科技平台资助项目(051005)

摘　　要：结合领域专家的经验知识,根据提升机制动系统故障树,完成了故障样本的收集与设计,然后用自组织特征映射(SOM)网络对制动系统的7种故障自动进行了分类,成功实现了第一层次的诊断;总结了制动系统子系统-液压站故障树,进行故障样本的收集与设计,然后用BP网络、BP网络状态分类器和Elman网络对液压站故障进行了第二层次的诊断,确定了故障原因和程度.对液压站故障的测试结果表明,这3种网络最后的结构和智能算法trainlm、输入、输出均能满足故障诊断与预测的要求;Elman网络的诊断性能较稳定,其隐含层神经元数对诊断性能的影响较小;故障测试精度由高到低依次是BP网络状态分类器、BP网络、Elman网络.Proposed in this paper is a two-level fault diagnosis procedure of hoist. In the first-level diagnosis, fault samples of hoist are collected and designed based on the experience and knowledge of experts and on a fault tree of the brake system, and seven kinds of failure modes of the brake system are automatically classified with SOM network. In the second-level diagnosis, a fault tree of sub-system-hydraulic station is built and the corresponding fault samples are collected and designed. Afterwards, the cause and degree of the hydraulic station fault are determined based on the diagnosis with BP network, BP network state classifier and Elman network. Test results of the hydraulic station fault show that （ 1 ） the structures, the intelligent algorithm trainlm, the inputs and outputs of the three above-mentioned networks all meet the requirements of fault diagnosis and prediction ; （2） Elman network is of the most stable diagnosis performance slightly affected by the number of hidden layer neurons; and （3） BP network state classifier is of the highest test precision while Elman network is of the lowest one.

关 键 词：制动系统 液压站 故障树 诊断 预测 

分 类 号：TP206.3[自动化与计算机技术—检测技术与自动化装置] TP389.1[自动化与计算机技术—控制科学与工程]