Abstract
Traditional multivariate statistical process monitoring algorithms focus on whether measurements are significantly shifted compared with the training data, but lack further analysis of the monitoring results. This results in frequent alarm triggering for process variations that do not require urgent operator attention, such as operating condition deviations, faults that are unrelated to key performance indicators (KPI), and faults that are compensated by the closed-loop system feedback mechanism. Machine downtime for every alarm leads to high economic losses for the plant. Therefore, it is important to perform Fault Risk analysis to identify security threats from process variations. A risk-oriented approach should be able to determine whether faults are associated with safety or quality risks, thereby reducing overhaul costs and increasing economic efficiency. In this study, a Fault Risk analysis framework is proposed for nonlinear dynamic processes based on a kernel dynamic regression (KDR) model. The framework consists of two algorithms: one is KDR for detecting process faults, and the other is KPI-related KDR (KPI-KDR) for detecting faults affecting product quality. The proposed approaches provide more reasonable and interpretable dynamic and static subspace decomposition, which facilitates further analysis of the monitoring results. First, the KDR concurrently detects normal operating condition deviations and process faults. Then, the KPI-KDR analyzes whether faults can be compensated by feedback mechanisms. Finally, a closed-loop continuous stirred tank reactor and real catalytic cracking unit data are used to validate the effective performance of the proposed algorithms.