Distributed Fault Detection for Large-Scale Systems: A Subspace-Aided Data-Driven Scheme With Cloud-Edge-End Collaboration
Biao Li, Ying Yang
Abstract
Unknown interaction items in the construction of distributed residual generators for large-scale systems will lead to the failure of existing data-driven fault detection (FD) methods based on subspace identification. To solve this problem, a subspace-assisted distributed FD scheme under the cloud-edge-end collaboration framework is proposed. For the residual generator constructed for each subsystem, the unknown input item is proved to be represented by the global system's input and output (I/O) data. In addition, based on the represented unknown input term, a data-driven form of the residual generator required for each subsystem is designed. Meanwhile, to eliminate the computing and storage burden caused by the global I/O data required by the represented unknown input item, a cloud-edge-end collaboration architecture is proposed and the corresponding tasks are deployed on the three sides of the cloud-edge-end, respectively. The effectiveness of the proposed method is analyzed and verified by numerical simulation and a real hot rolling case.