基于敏感度分析与概率神经网络的液压泵故障诊断方法研究  

Research on Fault Diagnosis Method of Hydraulic Pump Based on Sensitivity Analysis and PNN

作　　者：杜振东 赵建民[1] 张鑫[1] DU Zhendong;ZHAO Jianmin;ZHANG Xin(Ordnance Engineering College,Shijiazhuang Hebei 050003,China;Unit No.92667)

机构地区：[1]军械工程学院 [2]92667部队

出　　处：《机床与液压》2018年第19期165-169,共5页Machine Tool & Hydraulics

基　　金：河北省自然科学基金资助项目(E2015506012)

摘　　要：为了提高液压泵故障诊断的速度与准确性,提出了将敏感度分析与概率神经网络相结合的液压泵故障诊断方法。分析了不同状态下振动信号的时域图与频谱图,得出使用传统方法不易对液压泵进行故障诊断的结论。对各种状态下的振动信号提取特征参数,并对所提取特征参数进行敏感度分析。将敏感度高的特征参数以向量的形式输入概率神经网络进行训练和测试。实验表明:概率神经网络能快速、有效的诊断出液压泵的故障,节约诊断时间。将敏感度分析与概率神经网络相结合能提高概率神经网络诊断的正确率。A hydraulic fault diagnosis method that combined sensitivity analysis and probability neural network(PNN)is proposed to improve the speed and accuracy of the hydraulic pump fault diagnosis.First,the time-domain figure and the spectrum figure that under various states were analyzed to find using the traditional method to diagnose the fault of the hydraulic pump is difficult.Then,the feature parameters that under various states were extracted,and the sensitivity of the feature parameters were analyzed.Finally,constituted vectors with feature parameters that have the higher sensitivity,used the vectors to train PNN,and used the trained PNN to diagnose fault of the hydraulic pump.Experiment show that PNN can quickly and accurately diagnose the fault,save the diagnostic time.And combined sensitivity analysis and PNN can improve the correctness of PNN.

关 键 词：液压泵 敏感度分析 概率神经网络 故障诊断 

分 类 号：TP306[自动化与计算机技术—计算机系统结构]