NDLI: Process Fault Detection and Diagnosis Based on Principal Component Analysis

Content Provider	IEEE Xplore Digital Library
Author	Tao He Wei-Rong Xie Qing-Hua Wu Tie-Lin Shi
Copyright Year	2006
Description	Author affiliation: Sch. of Mech. Eng., Hubei Univ. of Technol., Wuhan (Tao He; Wei-Rong Xie; Qing-Hua Wu; Tie-Lin Shi)
Abstract	Conventional process fault detection and diagnosis technique need an in-depth comprehension and mastery in process mechanism models, which have to obtain very particular process transcendental knowledge and various physical and chemical parameters. It is very time-consuming and difficult for actual production processes. A novel process fault detection and diagnosis technique based on principal component analysis (PCA) is presented and discussed. The proposed method reduces the dimensionality of the original data set by the projection of the data set onto a smaller subspace defined by the principal components through PCA, and the multivariate statistical process control charts, for example, Hotelling $T^{2},$ Q and contribution charts are used to detect and diagnose the process faults. The monitoring performance of the proposed method to a typical continuous production process indicates that the fault diagnosis model constructed by PCA can efficiently be used to extract the main variable information of original data set independent of the process mechanism, and detect the abnormal change of the process
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3551
Ending Page	3556
File Size	363335
Page Count	6
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258550
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Fault diagnosis Principal component analysis Continuous production Covariance matrix Chemical processes Process control Condition monitoring Production systems Security
Content Type	Text
Resource Type	Article

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