Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology

A combined data-driven and observer-design methodology for fault detection and isolation (FDI) in hybrid process systems with switching operating modes is proposed in this work. The main contribution is to construct a unified framework for FDI by integrating Gaussian mixture models (GMM), subspace model identification (SMI), and results from unknown input observer (UIO) theory. Initially, a GMM is built to identify and describe the multimodality of hybrid systems by using the recorded input/output process data. A state-space model is then obtained for each specific operating mode based on SMI if the system matrices are unknown. An UIO is designed to estimate the system states robustly, based on which the fault detection is laid out through a multivariate analysis of the residuals. Finally, by designing a set of unknown input matrices for specific fault scenarios, fault isolation is carried out through the disturbance-decoupling principle from the UIO theory. A significant benefit of the developed framework is to overcome some of the limitations associated with individual model-based and data-based approaches in dealing with the problem of FDI in hybrid systems. Finally, the validity and effectiveness of the proposed monitoring framework are demonstrated using a simulation example.