上海交通大学学报

上海交通大学学报（自然版） ›› 2015, Vol. 49 ›› Issue (06): 775-779.

• 自动化技术、计算机技术 • 上一篇    下一篇

非高斯过程与微小故障的故障检测方法

郭天序,陈茂银,周东华   

  1. (清华大学 自动化系， 北京 100084)
  • 收稿日期:2015-03-26 出版日期:2015-06-29 发布日期:2015-06-29
  • 基金资助:

    国家自然科学基金（61490701, 61210012, 61290324）,清华大学自助科研计划资助项目

A Fault Detection Method of Non-Gaussian Processes and Small Shift

GUO Tianxu，CHEN Maoyin，ZHOU Donghua   

  1. (Department of Automation, Tsinghua University, Beijing 100084, China)
  • Received:2015-03-26 Online:2015-06-29 Published:2015-06-29

摘要：

摘要：  针对生产过程中数据服从非高斯分布的故障检测问题进行了讨论，给出了一个改进的基于稀疏表示的故障检测方法，并通过构建重构误差控制限和距离控制限区间，提高了基于稀疏表示的故障检测水平；给出了在统一框架下的微小故障检测方法，并通过在微小故障字典矩阵中引入时间常数t来提高针对微小故障的检测水平；通过两例数值仿真验证了方法的有效性，并与经典的基于主元分析的故障检测方法进行了对比.结果表明，所提出的方法在上述2种情况下的故障检测水平均超过基于主元分析的故障检测水平.
关键词：  稀疏表示； 非高斯过程； 微小故障； 故障检测
中图分类号：  TP 277文献标志码：  A

Abstract:

Abstract： In this paper, the fault detection of non-Gaussian processes was discussed. A modified sparse representationbased fault detection method was proposed. Two control thresholds  called reconstruction error control threshold (CLE) and distance control threshold (CLDint) were introduced, and the fault detection level was improved. Additionally, by introducing a time constant, a sparse representation-based small shift detection method was proposed based on the same framework above， enhancing the detection ability of small shift. Two numerical examples demonstrate the effectiveness of these two methods, and a comparison between the proposed methods and the classic PCA-based fault detection method shows that the proposed methods are superior.

Key words: sparse representation, non-Gaussian processes, small shift, fault detection