Fault detection based on weighted difference principal component analysis

Recently, multivariate statistical methods, such as principal component analysis (PCA), have drawn increasing attention for fault detection applications in industrial processes. However, industrial processes typically have complex multimodal and nonlinear characteristics. In these situations, the traditional PCA method performs poorly due to its assumption that the process data are linear and unimodal. To improve fault detection performance in nonlinear and multimode industrial processes, this paper proposes a new fault detection method based on weighted difference principal component analysis (WDPCA). Weighted difference principal component analysis first eliminates the multimodal and nonlinear characteristics of the original data by using the weighted difference method. Then, PCA is applied to the preprocessed data, neglecting the influences of multimodality and nonlinearity. Two numerical examples and an industrial application in a semiconductor manufacturing process are used to verify the effectiveness of WDPCA. The simulation results demonstrate that WDPCA shows better fault detection performance than the PCA, kernel principal component analysis (KPCA), independent component analysis (ICA), k‐nearest neighbor rule (kNN), and local outlier factor (LOF) methods.