Data-Driven Distributed Fault Detection and Fault-Tolerant Control for Large-Scale Systems: A Subspace Predictor-Assisted Integrated Design Scheme.

Abstract

Considering the influence of subsystem state interconnection in large-scale systems, the existing integrated design methods of data-driven fault detection (FD) and fault-tolerant control (FTC) that follow centralized architecture cannot be applied in distributed scenarios. To address this problem, this article proposes a subspace predictor-assisted framework to perform the data-driven integrated design of FD and FTC for large-scale systems. FD and FTC are organically combined through a subspace predictor framework. For the subspace predictor designed for each subsystem, no global input and output (I/O) data information is required but only the I/O data of the local and neighboring subsystems is used, thus realizing a distributed design. In addition, the integrated architecture of FD and FTC does not need any large-scale system mechanism information, and is completely driven by process I/O data. Two case studies including a numerical simulation example and cascaded CSTR verify the feasibility and effectiveness of the proposed data-driven distributed FD and FTC method.