Shuyu Ding

This article proposes a data-driven distributed Kalman filter (DKF)-based sensor fault isolation and estimation scheme for large-scale interconnected dynamic systems, composed of heterogeneous subsystems coupled through a directed topological graph. A local diagnosis unit (LDU) is established for each subsystem, where the data-driven DKF-based residual generator is constructed using local and neighboring process data, effectively decoupling the totally unknown interaction component. Subsequently, fully distributed sensor fault isolation is realized at the subsystem and element levels in simultaneous-fault cases. Both local and neighboring sensor fault isolation can be realized in the LDU, allowing the global system sensor fault isolation with only several key LDUs. Then, the data-driven DKF-based estimator is built in each LDU to estimate sensor faults occurring in multiple subsystems. The distributed Kalman gain is computed in a fully distributed manner, with stability analysis performed locally without overall system knowledge. Finally, the effectiveness and performance of the proposed scheme are validated through case studies on the power network system.

This article presents a data-driven distributed Kalman filter (DKF)-based fault diagnosis scheme, which can successfully detect actuator and sensor faults in large-scale dynamic systems, with heterogeneous subsystems interconnected through the directed topological graph. In the developed distributed framework, a local computing model (LCM) is formulated for each individual subsystem. In each LCM, based on the subspace identification method, the data-driven distributed Luenberger observer-based residual generator is constructed for each subsystem using local and neighboring process data. This offers an alternative expression of subsystem dynamics, supporting the design of more practical functions, for example, integrated monitoring and control design. The totally unknown interaction term is decoupled in the local residual, preventing the effect of actuator faults from propagating through the diagnostic network, so that the actuator fault isolation can be realized in each LCM. Then, the local noise covariance matrices are identified, and thus the data-driven DKF-based residual generator is formed, attaining improved detection performance by attenuating the effect of strong noises. Moreover, the adaptive configuration is developed for each subsystem, where the detector does not require retraining for changes in operating points or interconnection parameters, ensuring the continuity of the diagnostic process. Finally, case studies on the hot strip mill process verify the effectiveness and performance of the proposed method.

This paper is concerned with distributed fault estimation and fault-tolerant control for interconnected systems. Based on the local and interconnected information relevant to neighbors, a distributed fault estimation observer (DFEO) is constructed in a fully distributed fashion for each subsystem. The generated estimation errors are decoupled with external disturbances such that the DFEO has an asymptotical estimation feature. Then, using the estimated state and fault, a fault compensation based distributed fault-tolerant control (DFTC) law is designed for each subsystem. Sufficient conditions in the form of linear matrix inequalities (LMIs) are presented to guarantee the closed-loop system’s properties of stability and disturbance attenuation. Moreover, the proposed DFEOs and DFTC laws allow subsystems to be added or removed in a plug-and-play (PnP) fashion, which enhances the scalability of interconnected systems. Finally, the effectiveness and performance of the proposed method are demonstrated through simulations conducted on the power network system (PNS).