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Fault detection and diagnosis strategy based on a weighted and combined index in the residual subspace associated with PCA
Author(s) -
Zhang Cheng,
Gao Xianwen,
Xu Tao,
Li Yuan,
Pang Yujun
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2981
Subject(s) - principal component analysis , residual , subspace topology , fault detection and isolation , kernel principal component analysis , mean squared error , mathematics , statistic , statistics , norm (philosophy) , computer science , pattern recognition (psychology) , euclidean distance , algorithm , data mining , artificial intelligence , support vector machine , kernel method , actuator , political science , law
Process monitoring and diagnosis are crucial for efficient and optimal operation of a chemical plant. Most multivariate statistical process monitoring strategies, such as principal component analysis, kernel principal component analysis, and dynamic principal component analysis, take advantage of the squared prediction error statistic to monitor the state of samples in a residual subspace (RS). Squared prediction error is defined as the square of the 2‐norm of a residual vector, and it is calculated as the squared norm of the residual components. When the distributions of variables in an RS are quite different from one another, the detection ability of squared prediction error visibly declines. To accurately monitor the faults occurring in the RS, a new fault detection index based on a weighted combination of Hotelling's T 2 and squared Euclidean distance is developed in this paper. Principal component analysis is first introduced for dividing the original input space into a principal component subspace and an RS. Next, a weighted and combined index is implemented to monitor the variability of samples in the RS. In addition, a corresponding fault diagnosis strategy based on the contribution plot is also developed in this paper. The proposed method is tested on a numerical example and the Tennessee Eastman process. Simulation results show that the new index is effective in both fault detection and diagnosis.