Fault detection strategy of independent component-based k nearest neighbor rule
ZHANG Cheng
GAO Xian-wen
XU Tao
LI Yuan
PANG Yu-jun
Abstract：Fault detection based on k nearest neighbors (FD-kNN) method is able to improve the fault detection rate (FDR) in a process with nonlinear and multimode characteristic. Since some faults are caused by some abnormal change of latent variables and they are difficultly recognized through the observed variables, when FD-kNN is implemented directly in observed data set, its detection result is disappointed. Aiming to improve the fault detection ability of FD-kNN on abnormal change of latent variables, a k nearest neighbors fault detection strategy based on independent component analysis (ICA) is proposed in this paper. First, implement ICA in observed data set to obtain an IC matrix, in which all variables are independent. Then, the conventional FD-kNN is implemented to detect faults in the proposed IC matrix. When FD-kNN is implemented in IC matrix, it means that some latent variables are monitored by FD-kNN. Hence, the faults occurring on latent variables are able to be detected by FD-kNN. The efficiency of the proposed strategy is implemented in a simulated case and in the semiconductor manufacturing processes. The experimental results indicate that the proposed method outperforms PCA (principal component analysis), KPCA (kernel principal component analysis), k-nearest neighbor rule based on PCA (PC-kNN) and FD-kNN.
Keywords：k nearest neighborsindependent component analysisprincipal component analysisfault detectionbatch process
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 805-812 )