Journal
JOURNAL OF PROCESS CONTROL
Volume 114, Issue -, Pages 105-119Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2022.04.010
Keywords
Imprecise data; Spectral radius; Interval principal component analysis; Fault detection
Funding
- National Natural Science Foundation of China [61903272]
- Natural Science Foundation of Tianjin, China [20JCQNJC01670]
- Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China [ICT2022B62]
Ask authors/readers for more resources
The proposed SR-IPCA method effectively tackles uncertainties in complex industrial processes by defining an interval domain and extracting correlation structure with relative low computation complexity. The method significantly reduces false alarm rate and missed detection rate compared with traditional PCA methods like PCA, V-PCA, and C-PCA.
Data-driven process monitoring approaches like principal component analysis (PCA) have been widely used in many industrial processes, most of which assume that the data are precise and reliable. However, the data collected from actual processes are usually contaminated with uncertainties due to measurement noise, severe working scenarios and other reasons. Aiming at fault detection in the complex industrial processes with imprecise measurement data, a spectral radius-based interval PCA (SR-IPCA) method is proposed in this work. First, kernel density estimation (KDE)-based measurement error estimation strategy is presented to transform imprecise single-valued data into interval-valued data. Then, a spectral radius based eigen-decomposition method for interval matrix is developed to extract the process features by projecting high-dimensional interval data to low-dimensional space. Moreover, four monitoring statistics are defined to analyze process status and identify the fault. The proposed SR-IPCA method can effectively tackle the uncertainties by defining an interval domain and extract correlation structure with relative low computation complexity. The simulation results on the synthetic data sets and the Tennessee Eastman process (TEP) reveal that SR-IPCA significantly reduces the false alarm rate and the missed detection rate compared with PCA, vertices PCA (V-PCA) and centers PCA (C-PCA). (c) 2022 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available