NDLI: Data-Driven Design for static Model-Based fault diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Zhangming He Jiongqi Wang Chen Yin Haiyin Zhou Dayi Wang Yan Xing
Copyright Year	2015
Description	Author affiliation: Beijing Inst. of Control Eng., China Acad. of Space Technol., Beijing, China (Haiyin Zhou; Dayi Wang; Yan Xing) \|\| Coll. of Sci., Nat. Univ. of Defense Technol., Changsha, China (Zhangming He; Jiongqi Wang; Chen Yin)
Abstract	This paper focuses on Data-Driven Design FOR Model-Based fault diagnosis, called D34MB for short. When the objective model is static, LVD (Latent Variable Detection) methods can be realized based on the LVE (Latent Variable Extraction) and LVR (Latent Variable Regression) techniques. A unified weight-framework for D34MB are proposed in this paper, which shows that all D34MB methods share the same procedures, i.e., LVE, LVR and LVD. The detection theorems shows that D34MB methods based on RRR (Rank Reduction Regression) and CCA (Canonical Correlation Analysis), compared with PCA (Principal Component Analysis) and PLS (Partial Least Square), tend to ensure higher calibration accuracy in terms of MSE as well as better detection performance in terms of FDR (Fault Detection Rate). In the case study, TEP (Tennessee Eastman Process) validates the correctness of our theoretical results.
Starting Page	1989
Ending Page	1994
File Size	1756809
Page Count	6
File Format	PDF
e-ISBN	9781467371896
DOI	10.1109/CAC.2015.7382831
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Fault detection Latent variable regression Partial least square Data models Yttrium Reduced rank regression Canonical correlation analysis Principal component analysis
Content Type	Text
Resource Type	Article

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