Data-driven design of fault detection and isolation method for distributed homogeneous systems

Abstract

Driven by the increasing needs in large-scale industrial processes for maintaining stable operation using process data, a data-driven fault detection and isolation (FDI) method for distributed homogeneous systems is proposed in this paper. To this end, the original distributed homogeneous system is first decomposed into several separate subsystems using invertible transformations of the state, input, and output variables. On this basis, the fault detection method according to the one-step identification of the stable kernel representation (SKR) is developed for the transformed subsystems. It is followed by the fault estimation for the transformed subsystems along with implementation on fault isolation for the original distributed system. Experiments comparing the proposed FDI method and the centralized FD method are performed using a numerical example. In this specific example, the proposed FDI method has a computational complexity of 2.32×107, while the centralized FD method has a computational complexity of 9.31×107. This shows the effectiveness and advantages of the proposed method.