Fault Detection of Industrial Process Based on Dynamic Nearest&nbsp;Neighborhood Standardization of Principal Polynomial Analysis

Control Engineering of China ›› 2022, Vol. 29 ›› Issue (2) : 198-206.

Fault Detection of Industrial Process Based on Dynamic Nearest Neighborhood Standardization of Principal Polynomial Analysis

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Abstract

Aiming at the dynamic, multi-modal and nonlinear characteristics of complex industrial processes, a fault detection algorithm based on dynamic nearest neighborhood ntandardization and principal components analysis (DNSPPA) is proposed in this paper. Firstly, a certain length of time window is set to describe the temporal correlation between the sample points. Secondly, the local nearest neighbor set of the sample in the spatial direction in the time window is found, and the nearest neighbor set is used to standardize the data sample. Finally, the PPA model is established based on the standardized data to calculate the statistics and establish the control limit for fault detection. DNSPPA method can solve the problem of dynamic time sequence and multimodal data center drift in complex industrial processes, so as to reduce the impact of multi-modal structure on PPA detection performance. To demonstrate its effectiveness and superiority, the proposed DNSPPA method is tested by multi-modal nonlinear numerical examples with dynamic characteristics and penicillin data. Compared with principal component analysis (PCA) and principal polynomial analysis (PPA), the proposed method in this paper can effectively detect the faults and improve the detection rate.

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Multi-modal process / nonlinear process / dynamic modeling / principal polynomial analysis / fault detection

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LI Yuan, ZHANG Yi-nan, FENG Li-wei. Fault Detection of Industrial Process Based on Dynamic Nearest Neighborhood Standardization of Principal Polynomial Analysis[J]. Control Engineering of China, 2022, 29(2): 198-206