掘进机液压系统故障检测方法研究与仿真  

Research and Simulation of Fault Detection Method for Hydraulic System of Tunneling Machine

作　　者：王大虎[1] 王敬冲 陈文博[1] 史艳楠 

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《计算机仿真》2016年第2期448-451,共4页Computer Simulation

摘　　要：部分断面掘进机作为现代化煤矿掘进的主力机械,液压系统构成复杂,易在恶劣的工作环境中发生液压泵损坏、液压马达噪声过大、单向阀泄漏等故障,但是,这些故障特征之间存在较强的关联性,且故障之前不存在准确的区分标准,传统的诊断方法没有利用这种关联判断故障,仅以简单阀值理论进行判断,难以满足智能诊断要求。为此提出一种采用模糊理论与BP神经网络结合的诊断方法,通过模糊理论对故障关联性产生的模糊性进行判断,再利用BP神经网络进行分析,以柱塞泵为例建立部分断面掘进机液压系统故障诊断的模型,对液压泵的故障状态进行诊断。与标准BP神经网络模型诊断方法对比研究表明,模糊神经网络模型收敛速度快,精度高,更适用于部分断面掘进机液压系统的故障诊断。In this paper,we presented a diagnosis method based on combining fuzzy theory and BP neural network. In this method,the fuzziness generated by the fault relevance was judged with fuzzy theory. Then a BP neural network was designed to carry out the analysis. Taking the plunger pump as an example,the fault diagnosis model for hydraulic system of the partial-size tunneling machine can be established,to make fault diagnosis for hydraulic pump. Compared with the diagnostic method based on standard BP neural network model,the research shows that the fuzzy neural network model has fast convergence speed and high precision,which is more suitable for the fault diagnosis of hydraulic system of partial-size tunneling machine.

关 键 词：部分断面掘进机 液压系统 故障诊断 模糊理论 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]