Subspace-Aided Data-Driven Robust Distributed Detection With Cooperative Fault Sensing for Large-Scale Systems

Traditional data-driven fault detection methods based on subspace identification encounter difficulties when performing distributed fault detection on large-scale systems, mainly due to the presence of the unknown interaction term in the residual generator constructed for each subsystem. To tackle these problems, this article proposes a robust distributed fault detection method based on subspace identification for large-scale systems. First, the initial identification error of the residual generator constructed by each subsystem is obtained by using the input and output data information of the local and neighbors, and then a one-step correction theorem is introduced to minimize the error further. In addition, a robust residual generator that is sensitive to faults and robust to noise is constructed by studying all dimensions of the parity space. The effectiveness and superiority of the proposed method are illustrated by comparing the existing methods in two case studies of numerical simulation and real finishing mill system in hot strip rolling.