Plant-Wide Distributed Adaptive Probability Density Analysis Method for Incipient Fault Detection

In plant-wide process, the incipient fault may be ignored due to its small amplitude. In order to realize the incipient fault detection for the large-scale time-varying process, a novel distributed adaptive probability density analysis method is introduced in this article. First, a data-driven process decomposition and subblock division strategy is proposed, and the adaptive modeling sample set is constructed based on the online data in each subblock, which converges the information related to the current operating state. Afterward, the key latent variable which contributes to the online variation is selected for the process modeling, and a probability density based monitoring indicator is constructed for the incipient fault detection, which can effectively identify the unusual data distribution in the online testing samples. After the subblock modeling and monitoring, the Bayesian fusion strategy is introduced for the subblock decision integration. Finally, two case studies are used to illustrate the advantages of the proposed method.