首页> 外文学位 >A fault detection scheme for modeled and unmodeled faults in a simple hydraulic actuator system using an extended Kalman filter.
【24h】

A fault detection scheme for modeled and unmodeled faults in a simple hydraulic actuator system using an extended Kalman filter.

机译:在使用扩展卡尔曼滤波器的简单液压执行器系统中,用于模型化和非模型化故障的故障检测方案。

获取原文
获取原文并翻译 | 示例

摘要

In this work an extended Kalman filter (EKF) is used to detect a variety of faults in a simple hydraulic actuator system. The system includes a constant supply pressure feeding a spool valve, which controls a double-rod cylinder with no applied load. Much interest exists in detecting faults in their early stages in the hopes that machine downtime and repair costs can be kept to a minimum. This EKF model employs two different techniques for identifying the presence of system faults. In one case, parameters of interest are included in the state-space model as augmented states. Faults are then introduced into these new states, and the EKF successfully detects the faults by tracking the new post-fault parameter values. The second method is an indirect approach for identifying unmodeled faults. These faults become apparent through analysis of the difference between a state measurement and estimate, known as error residual data. It is shown that, for this simple hydraulic system, this extended Kalman filter detects system faults confidently and promptly.
机译:在这项工作中,扩展的卡尔曼滤波器(EKF)用于检测简单的液压执行器系统中的各种故障。该系统包括一个恒定的供应压力,该压力供应给滑阀,该滑阀在没有施加负载的情况下控制双杆缸。人们对在早期阶段检测故障抱有浓厚的兴趣,希望将机器的停机时间和维修成本降至最低。该EKF模型采用两种不同的技术来识别系统故障的存在。在一种情况下,感兴趣的参数作为增强状态包含在状态空间模型中。然后将故障引入这些新状态,并且EKF通过跟踪新的故障后参数值成功检测到故障。第二种方法是用于识别未建模故障的间接方法。通过分析状态测量值与估计值之间的差异(称为误差残差数据),这些故障变得显而易见。结果表明,对于这种简单的液压系统,这种扩展的卡尔曼滤波器能够可靠而迅速地检测到系统故障。

著录项

  • 作者

    Ryerson, Cody.;

  • 作者单位

    University of Missouri - Columbia.;

  • 授予单位 University of Missouri - Columbia.;
  • 学科 Engineering Mechanical.
  • 学位 M.S.
  • 年度 2006
  • 页码 63 p.
  • 总页数 63
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号