Novel variation mode decomposition integrated adaptive sparse principal component analysis and it application in fault diagnosis

Zhiqiang Geng; Xiaoyan Duan; Yongming Han; Fenfen Liu; Wei Xu

doi:10.1016/j.isatra.2021.11.002

Novel variation mode decomposition integrated adaptive sparse principal component analysis and it application in fault diagnosis

ISA Trans. 2022 Sep;128(Pt B):21-31. doi: 10.1016/j.isatra.2021.11.002. Epub 2021 Nov 18.

Authors

Zhiqiang Geng¹, Xiaoyan Duan¹, Yongming Han², Fenfen Liu¹, Wei Xu³

Affiliations

¹ College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China.
² College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; Engineering Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China. Electronic address: hanym@mail.buct.edu.cn.
³ State Key Laboratory of Chemical Safety Control, SINOPEC Research Institute of Safety Engineering Co., Ltd., Qingdao 26600, China.

PMID: 34857354
DOI: 10.1016/j.isatra.2021.11.002

Abstract

The sparse principal component analysis (SPCA) is widely used in the fault detection for nonlinear complex chemical processes in recent years. However, insufficient data processing, fixed models and fault type single classification cannot be used in the time-varying process. Therefore, a novel adaptive sparse principal component analysis (ASPCA) algorithm fused with improved variation mode decomposition (IVMD) (ASPCA-IVMD) is proposed for fault detection in chemical processes. The bat algorithm is innovatively integrated to optimize the parameters of the variable modulus decomposition. Then the optimized parameters are used for data preprocessing to suppress noise. In addition, based on the traditional SPCA, the threshold calculation is fused to realize the adaptive selection of principal components. After the principal components are determined, T² and Q statistics are used for fault detection. Finally, the proposed method is verified by the Tennessee Eastman process case. The results demonstrate that the proposed method can select the principal components adaptively according to the data for having the real-time property of chemical process. Meanwhile, compared with traditional methods (principal component analysis, sparse principal component analysis, deep belief network integrating dropout, adaptive unscented Kalman filter integrating radial basis function and sparse deep belief network), the detection rate of the ASPCA-IVMD method is more than 99%, which shows superiority.

Keywords: Adaptive sparse principal component analysis; Bat algorithm; Chemical processes; Fault diagnosis; Threshold method; Variation mode decomposition.