| 独立元子空间算法及其在故障检测上的应用 |
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| 引用本文： | 张沐光,宋执环.独立元子空间算法及其在故障检测上的应用[J].化工学报,2010,61(2):425-431. |
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| 作者姓名： | 张沐光 宋执环 |
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| 作者单位： | 工业控制技术国家重点实验室，浙江大学工业控制研究所 |
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| 基金项目： | 国家自然科学基金，国家高技术研究发展计划(863计划) |
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| 摘 要： | 针对高维数据建模问题,提出一种独立元子空间算法(ICSM),作为一种新的集成学习算法,ICSM利用独立元在不同变量上的贡献度来选取子空间,符合了集成学习的要求,具备了明确的物理意义,有效地克服了随机子空间算法(RSM)的主要缺点。在此基础上,进一步将ICSM应用于工业过程监控,提出了一种新的ICSM-PCA故障检测算法。首先在各个子空间内分别建立相应的PCA监测模型,然后根据T~2和SPE统计量的值计算出集成时各自的权重,最后构造两个集成统计量对工业过程进行监测。通过在Tennessee Eastman(TE)模型上的仿真研究,说明提出的算法具有较好的建模效果和故障检测能力。
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| 关 键 词： | 集成学习 随机子空间方法 主元分析 故障检测 |
| 收稿时间： | 2009-6-9 |
| 修稿时间： | 2009-10-29
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Independent component subspace method and its application to fault detection |
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| ZHANG Muguang,SONG Zhihuan.Independent component subspace method and its application to fault detection[J].Journal of Chemical Industry and Engineering(China),2010,61(2):425-431. |
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| Authors: | ZHANG Muguang SONG Zhihuan |
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| Abstract: | To handle the modeling problem for high-dimension data, the independent component subspace method (ICSM) was proposed.As a new ensemble learning method, ICSM could overcome the main drawback of the random subspace method.It constructed subspaces according to independent components (ICs) contributions on different process variables.As a result, the modeling requirement of the ensemble learning method was satisfied, and its physical meaning was also well presented.Moreover, a new fault detection method named ICSM-PCA was also developed.Firstly, PCA monitoring models were build on different subspaces, then the weighted value of each model was computed based on T~2 and SPE statistics.Finally,two ensemble statistics could be built for monitoring industrial processes.A case study of the Tennessee-Eastman (TE) process illustrated that the proposed method showed good modeling performance and exhibited satisfactory fault detection ability. |
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| Keywords: | ensemble learning random subspace method principal component analysis fault detection |
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