Collaborative Diagnosis of Spatiotemporal Faults and Sensor Anomalies in Parabolic Distributed Parameter Systems
针对热传导等工业过程中时空耦合的分布参数系统，提出一种基于模型的协同诊断框架，能同时检测和隔离系统故障与传感器异常，并通过数值仿真验证了有效性。
Many industrial processes, such as heat transfer and chemical diffusion reactions, are typical distributed parameter systems (DPSs) characterized by strong spatiotemporal (S-T) coupling. Any component within these systems may malfunction and result in significant safety risks. This article proposes a model-based framework for the collaborative diagnosis of S-T faults and sensor anomalies in DPSs. First, based on the reduced-order model obtained through the spectral method, two sets of observers are established for process faults and sensor anomalies, respectively. Fault detection and isolation (FDI) algorithms are developed by leveraging the characteristics of these two fault types. Next, using an unknown input observer (UIO), a cooperative fault estimation algorithm capable of handling the coexistence of both fault types is designed. The stability and convergence of the proposed method are ensured through the Lyapunov direct method. Finally, numerical simulations are conducted on a heat-transfer rod. The results demonstrate that the FDI algorithm can detect and isolate S-T faults and sensor anomalies effectively. Moreover, the root-mean-square error (RMSE) of the intensity estimation remains below 0.31, further verifying the effectiveness of the proposed collaborative diagnosis algorithm.