期刊
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
卷 13, 期 1, 页码 122-133出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2015.2417882
关键词
Data-driven modeling; fault diagnosis; feature extraction; Fisher discirminant analysis (FDA); kernel method; manifold learning
资金
- National Natural Science Foundation of China [61273164, 61034005, 61403042, 61403044]
- National High Technology Research and Development Program of China [2012AA040104]
- Fundamental Research Funds for the Central Universities [N130104001, N120504002]
- Liaoning Province Natural Science Foundation of China [2013020042]
- Education Department of Liaoning Province of China [L2013423]
Though Fisher discriminant analysis (FDA) is an outstanding method of fault diagnosis, it is usually difficult to extract the discriminant information in a complex industrial environment. One of the reasons is that, in such an environment, the discriminant information can not been extracted entirely due to the disturbances, non-Gaussianity and nonlinearity. In this paper, a method named Joint Fisher discriminant analysis (JFDA) is proposed to address the issues. First, JFDA removes outliers caused by disturbances according to the energy density of each datum. Then, for the non-Gaussianity and weakly nonlinearity, the novel scatter matrices are defined to extract both of the local and global discriminant information based on the manifold learning. Finally, the kernel JFDA (KJFDA) is investigated to hold the manifold assumption because the strongly nonlinearity may weaken the assumption and cause overlapping. The proposed method is applied to the Tennessee Eastman process (TEP). The results demonstrate that KJFDA shows a better performance of fault diagnosis than other improved versions of FDA.
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