上海交通大学学报

上海交通大学学报（自然版）

• 机械工程 • 上一篇    下一篇

电液伺服阀状态在线特征提取和异常检测方法

郝圣桥，许黎明，沈伟，王建楼
  

  1. (上海交通大学 机械与动力工程学院， 上海 200240）
  • 收稿日期:2009-10-16 修回日期:1900-01-01 出版日期:2010-12-31 发布日期:2010-12-31

Online Fault Feature Extraction and Abnormality Detection of ElectroHydraulic Servo Valve’s Condition

HAO Shengqiao,XU Liming,SHEN Wei,WANG Jianlou
  

  1. (School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
  • Received:2009-10-16 Revised:1900-01-01 Online:2010-12-31 Published:2010-12-31

摘要： 基于电液伺服阀的动态工作特性，提出一种新的特征提取方法.将一类支持向量机用于电液伺服阀的在线异常检测，通过交叉有效性估计学习方法优化一类向量机参数，并与神经网络的诊断结果进行对比.实验结果表明，所提出的特征提取方法能够有效地提取电液伺服阀的动态特征信息，同时利用一类支持向量机优良的泛化能力能够有效地对电液伺服阀的异常状态进行识别.

关键词: 电液伺服阀, 特征提取, 在线异常检测, 一类支持向量机

Abstract: An online methodology of abnormality detection of electrohydraulic servo valve’s working condition was studied. A new feature extraction method based on the valve’s dynamic characteristics was proposed. One class support vector machine (SVM) was adopted to identify the working condition of the valve and the SVM parameters were optimized by crossvalidity estimation learning means. The results indicate that the proposed feature extraction method can effectively extract the fault features of the valve and its abnormal condition can be more effectively identified by taking advantage of oneclass support vector machine’s generalization ability than by the neural network, which lays the foundation for online faults diagnosis of electrohydraulic servo valve.

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