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Fault detection method based on principal component difference associated with DPCA
Author(s) -
Zhang Cheng,
Guo Qingxiu,
Li Yuan
Publication year - 2019
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.3082
Subject(s) - principal component analysis , subspace topology , pattern recognition (psychology) , k nearest neighbors algorithm , fault detection and isolation , artificial intelligence , computer science , autocorrelation , residual , data set , nonlinear system , mathematics , algorithm , statistics , physics , quantum mechanics , actuator
Aiming at the fault detection in a dynamic process with nonlinear or multimodal features, a new fault detection strategy based on principal component difference associated with dynamic PCA (Diff‐DPCA) is proposed in this paper. First, augment the training data set using the time lag, and then obtain an augmented training data set. Second, find the K‐nearest neighbor set of each sample in the augmented training set and calculate the mean of the K nearest neighbor set. Third, calculate the loading and score matrices of the augmented training data set using PCA, and then calculate the scores of the proposed mean above using the loading matrix. Next, calculate the difference between the scores of a sample and its corresponding mean. Finally, 2 new statistics are built in difference subspace and residual subspace respectively. Principal component difference associated with dynamic PCA, which inherit the ability of DPCA monitoring a dynamic process, can improve the fault detection rate in the process with multimodal or nonlinear characteristics; meanwhile, the new statistics present low autocorrelation levels. The proposed method in this paper and other traditional methods are applied to detect faults in 2 cases and the Tennessee Eastman process, such as PCA, DPCA, and FD‐KNN. The experimental results indicate that the proposed method outperforms the conventional PCA, DPCA, and FD‐KNN.