Distributed monitoring of nonlinear plant-wide processes based on GA-regularized kernel canonical correlation analysis

Wenhao Jin, Wenjing Wang, Yang Wang, Zhixing Cao and Qingchao Jiang

Reliability Engineering and System Safety, 2024, vol. 252, issue C

Abstract: Fault detection and diagnosis is important for ensuring process safety and is gaining increasing attention in the system safety field. A regularized kernel canonical correlation analysis (RKCCA) approach is proposed for monitoring nonlinear plantwide processes. For each local unit, genetic algorithm (GA)-based regularization is performed to determine the communication variables from neighboring units, which preserves the maximum correlations and eliminates the irrelevant variables. Then variables from a local unit and the communication variables are mapped into high-dimensional feature spaces, and the feature space of the local unit is decomposed into three orthogonal subspaces, namely the residual subspace, the inner subspace, and the outer-related subspace. Monitoring statistics to identify both the process status and the characteristic of a detected fault are constructed. The proposed RKCCA-based monitoring method considers both the information of a local unit and the beneficial information of related units to facilitate fault detection, thereby exhibiting superior performance to some state-of-the-arts methods. Applications on the Tennessee Eastman benchmark process and an industrial tail gas treatment process demonstrate the superiority of RKCCA monitoring.

Keywords: System safety; Data-driven fault diagnosis; Nonlinear plant-wide processes; Kernel canonical correlation analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024004939

DOI: 10.1016/j.ress.2024.110421

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