A distributed principal component regression method for quality-related fault detection and diagnosis

Modern industrial processes are confronted with a large-scale challenge in recent years. In this paper, a novel distributed kernel principal component regression (DKPCR) approach is proposed to study the plant-wide problem of quality-related process monitoring. Along with reducing the scale of abundant measurements, the proposed approach also focuses on the robust issues that arise from the large outliers. In the local phase, every agent of the DKPCR technique processes the data of the partial sections and sends the selected information to the centralized mainframe. The engineer gathers the data from the local agents and then makes a decision based on the Bayesian inference. Additionally, the corresponding weight diagnosis approach is devised to isolate the fault-relevant variables under the smear effect by virtue of the detection results and information. In the end, the Tennessee Eastman case (TEC) and the three-phase flow system (TPFS) are leveraged to demonstrate the detection and diagnosis performance of the proposed approach. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a novel distributed kernel principal component regression (DKPCR) approach to address quality-related process monitoring in modern industrial processes. The approach reduces data scale and tackles robustness issues caused by large outliers, and involves Bayesian inference and weight diagnosis methods for data processing and fault variable isolation.