东北大学学报(自然科学版) ›› 2013, Vol. 34 ›› Issue (6): 761-765.DOI: -

• 信息与控制 •    下一篇

基于故障特征时段识别的间歇过程故障诊断方法

王姝1,赵珍2,常玉清1,谭帅1   

  1. (1.东北大学信息科学与工程学院,辽宁沈阳110819;2.中国民航大学航空自动化学院,天津300300)
  • 收稿日期:2012-12-24 修回日期:2012-12-24 出版日期:2013-06-15 发布日期:2013-12-31
  • 通讯作者: 王姝
  • 作者简介:王姝(1979-),女,辽宁鞍山人,东北大学讲师;常玉清(1973-),女,辽宁沈阳人,东北大学教授.
  • 基金资助:
    国家自然科学基金资助项目(61074074,61174130);中央高校基本科研业务费专项资金资助项目(N110304010);中国民航大学科研启动基金资助项目(2012QD22X).

Fault Diagnosis Method for Batch Process Based on Identification of Fault Feature Phases

WANG Shu1, ZHAO Zhen2, CHANG Yuqing1, TAN Shuai1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. College of Aerospace Automation, Civil Aviation University of China, Tianjin 300300, China.
  • Received:2012-12-24 Revised:2012-12-24 Online:2013-06-15 Published:2013-12-31
  • Contact: WANG Shu
  • About author:-
  • Supported by:
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摘要: 间歇过程的多时段操作特性使得某一类型故障可能在一个或多个子操作时段具有明显表征,而在其他时段没有故障表征,即故障具有其相应的特征时段.提出了一种基于故障特征时段识别的故障诊断方法,通过对历史故障数据以及正常数据质心分布特征,识别历史故障的特征时段.利用多向Fisher判别分析(MFDA)方法分别建立对应的故障诊断模型,从而将故障诊断的搜索空间深入到特定的特征时段,提高了模型的诊断性能.仿真实验验证了该方法的可行性和有效性.

关键词: 间歇过程, 多时段, 故障特征时段, 多向Fisher判别分析, 故障诊断

Abstract: Because of the multiplicity of operation phases in batch process, faults may have obvious features in one or more specific operation phases, but do not show any features in other operation phases. That is, faults have corresponding feature phases. A fault diagnosis method based on identification of fault feature phases was proposed. Identification of fault feature phases was realized by comparing the differences between the centroids of historical faulty data set and the normal data set. Different fault diagnosis models were respectively developed based on multiway Fisher discriminant analysis (MFDA) to reduce the search space to specific fault feature phases and improve the diagnosis performance of the models. Simulation experimental results showed the feasibility and validity of the proposed method.

Key words: batch process, multiphase, fault feature phase, multiway Fisher discriminant analysis (MFDA), fault diagnosis

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