Metric Learning Method Aided Data-Driven Design of Fault Detection Systems

被引:1
作者
Yan, Guoyang [1 ]
Mei, Jiangyuan [1 ]
Yin, Shen [1 ]
Karimi, Hamid Reza [2 ]
机构
[1] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150001, Heilongjiang, Peoples R China
[2] Univ Agder, Fac Sci & Engn, Dept Engn, N-4898 Grimstad, Norway
基金
中国博士后科学基金;
关键词
D O I
10.1155/2014/974758
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Fault detection is fundamental to many industrial applications. With the development of system complexity, the number of sensors is increasing, which makes traditional fault detection methods lose efficiency. Metric learning is an efficient way to build the relationship between feature vectors with the categories of instances. In this paper, we firstly propose a metric learning-based fault detection framework in fault detection. Meanwhile, a novel feature extraction method based on wavelet transform is used to obtain the feature vector from detection signals. Experiments on Tennessee Eastman (TE) chemical process datasets demonstrate that the proposed method has a better performance when comparing with existing methods, for example, principal component analysis (PCA) and fisher discriminate analysis (FDA).
引用
收藏
页数:9
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