基于独立子空间算法与集成策略的仪表微小故障诊断方法

doi:10.11772/j.issn.1001-9081.2013.07.2063

计算机应用 ›› 2013, Vol. 33 ›› Issue (07): 2063-2066.DOI: 10.11772/j.issn.1001-9081.2013.07.2063

基于独立子空间算法与集成策略的仪表微小故障诊断方法

胡吉晨¹,黄国勇¹,邵宗凯¹,²,王晓东¹,²,邹金慧¹,²

1. 昆明理工大学信息工程与自动化学院，昆明 650500
2. 云南省矿物管道输送工程技术研究中心，昆明 650500

收稿日期:2013-01-09 修回日期:2013-02-17 发布日期:2013-07-06 出版日期:2013-07-01
通讯作者: 黄国勇
作者简介:胡吉晨(1988-)，男，安徽马鞍山人，硕士研究生，主要研究方向：基于数据驱动的仪表故障诊断；黄国勇(1977-)，男，湖北天门人，副教授，博士，主要研究方向：工业过程故障诊断、工业过程智能控制；邵宗凯(1974-)，男，云南保山人，副教授，博士，主要研究方向：智能交通联网监控、电机控制。
基金资助:
国家自然科学基金资助项目(51169007);云南省科技计划项目(2010DH004，2011DA005，2011FZ036);云南省中青年学术和技术带头人后备人才培养计划项目(2011CI017);云南省教育厅基金资助项目(2011Y386)

Small fault detection method of instruments based on independent component subspace algorithm and ensemble strategy

HU Jichen¹,HUANG Guoyong¹,SHAO Zongkai¹,²,WANG Xiaodong¹,²,ZOU Jinhui¹,²

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650500, China
2. Yunnan Engineering Research Center for Mineral Pipeline Transportation, Kunming Yunnan 650500, China

Received:2013-01-09 Revised:2013-02-17 Online:2013-07-06 Published:2013-07-01
Contact: HUANG Guoyong

摘要/Abstract

摘要： 针对流程工业中多仪表微小故障难以检测的问题，利用独立元分析(ICA)提取仪表变量的独立元信息，根据独立元贡献度矩阵构建独立元子空间，并分别在每个独立元子空间上根据不同的贡献率选择独立元个数，得出三个统计量及其控制限，建立故障检测模型。再综合所有子空间故障检测模型的检测结果，根据实际需求制定集成故障检测策略，最后通过贡献度算法对故障源进行识别和分离。对Tennessee Eastman过程数据的仿真实验结果表明独立子空间算法提高了微小故障的检测精度，在流程工业中多仪表故障诊断中配合不同的集成故障检测策略在应用中更具有灵活性。

关键词: 仪表故障检测, 独立元子空间, 独立元贡献度, 集成策略

Abstract: To solve the problem of small fault detection of instruments in process industry, independent components were extracted by Independent Component Analysis (ICA) from instruments recorded data. And independent component subspaces were established according to the contribution matrix. Fault detection model was constructed in each independent component subspace with statistical variables. A proper ensemble strategy was chosen by combining all the fault detection results. Finally, the instrument with fault was located by contribution algorithm. The simulation results with TE (Tennessee Eastman) process show that this method has higher precision on small fault detection and more flexibility with proper ensemble strategy.

Key words: instrument fault detection, independent component subspace, independent component contribution, ensemble strategy

中图分类号:

TP277

胡吉晨黄国勇邵宗凯王晓东邹金慧. 基于独立子空间算法与集成策略的仪表微小故障诊断方法[J]. 计算机应用, 2013, 33(07): 2063-2066.

HU Jichen HUANG Guoyong SHAO Zongkai WANG Xiaodong ZOU Jinhui. Small fault detection method of instruments based on independent component subspace algorithm and ensemble strategy[J]. Journal of Computer Applications, 2013, 33(07): 2063-2066.

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基于独立子空间算法与集成策略的仪表微小故障诊断方法

Small fault detection method of instruments based on independent component subspace algorithm and ensemble strategy

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