| Adaptive partitioning PCA model for improving fault detection and isolation☆ |
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| 引用本文： | Kangling Liu,Xin Jin,Zhengshun Fei,Jun Liang. Adaptive partitioning PCA model for improving fault detection and isolation☆[J]. 中国化学工程学报, 2015, 23(6): 981-991. DOI: 10.1016/j.cjche.2014.09.052 |
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| 作者姓名： | Kangling Liu Xin Jin Zhengshun Fei Jun Liang |
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| 作者单位： | 1.State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;2.School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China |
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| 基金项目： | Support by the National Natural Science Foundation of China，the Research Fund for the Doctoral Program of Higher Education in China，Zhejiang Provincial Science and Technology Planning Projects of China |
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| 摘 要： | In chemical process, a large number of measured and manipulated variables are highly correlated. Principal com-ponent analysis (PCA) is widely applied as a dimension reduction technique for capturing strong correlation un-derlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physical y and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect. The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
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| 关 键 词： | Adaptive partitioning Fault detection Fault isolation Principal component analysis |
| 收稿时间： | 2014-04-25 |
Adaptive partitioning PCA model for improving fault detection and isolation |
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| Kangling Liu,Xin Jin,Zhengshun Fei,Jun Liang. Adaptive partitioning PCA model for improving fault detection and isolation[J]. Chinese Journal of Chemical Engineering, 2015, 23(6): 981-991. DOI: 10.1016/j.cjche.2014.09.052 |
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| Authors: | Kangling Liu Xin Jin Zhengshun Fei Jun Liang |
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| Affiliation: | 1.State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China;2.School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China |
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| Abstract: | In chemical process, a large number of measured and manipulated variables are highly correlated. Principal com-ponent analysis (PCA) is widely applied as a dimension reduction technique for capturing strong correlation un-derlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physical y and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect. The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method. |
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| Keywords: | Adaptive partitioning Fault detection Fault isolation Principal component analysis |
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