Process monitoring methods play a crucial role in identifying equipment malfunctions and instrument failures, as well as in maintaining process safety and product quality. Selecting the right approach for fault detection and diagnosis is therefore vital. Several localization methods based on Kernel Principal Component Analysis (KPCA) exist, such as the partial localization approach, which is effective at detecting anomalies but does not always pinpoint faults precisely. This method often identifies a suspicious area or group of variables without isolating the exact source of the fault. In complex systems such as chemical reactors, it can produce false positives or incorrect localizations if the data are noisy or if the fault affects multiple correlated variables. Conversely, the reconstruction-based contribution approach, when integrated with Kernel Principal Component Analysis (KPCA), is both widely documented in the literature and highly effective for fault localization. This method first identifies anomalies using the Hotelling’s T2 statistic and Q (squared prediction error) statistic, then analyzes the contributions of individual variables to these indices in order to isolate the fault. However, the convergence of the optimization algorithm using the T2 index is not guaranteed. To address this limitation, we introduce RBC-KGLRT, a novel localization framework that integrates reconstruction-based contribution with KPCA and the Generalized Likelihood Ratio Test in its kernel form to improve both precision and reliability in localization tasks. This work transforms traditional KPCA and reduced-rank KPCA fault detection approaches—enhanced by the KGLRT metric—into a powerful fault localization solution through the reconstruction-based contribution (RBC) method. Its effectiveness is rigorously evaluated using the Tennessee Eastman Process (TEP), a widely recognized simulation benchmark in process control and chemical engineering.
Lahdhiri et al. (Mon,) studied this question.