Fault diagnosis of a sensor network

This paper proposes a novel fault diagnosis scheme for a sensor network of a cascade, parallel and feedback combination of subsystems. The objective is to detect and isolate a fault in any of the subsystems and measurement sensors which are subject to disturbances and measurement noise. A bank of Kalman filters (KF) is employed to detect and isolate faults. Each KF is driven by either a pair (a) of consecutive sensor measurements or (b) of a reference input and a measurement. It is shown that the KF residual is an indicator of a fault in subsystems and sensors located in the path between the pair of the KF's input. The simple and efficient procedure proposed here analyzes each of the associate paths and leads to the fault detection and isolation. The scheme is successfully evaluated on several simulated examples and a physical fluid system exemplified by a benchmarked laboratory-scale two-tank system to detect and isolate faults including sensor, actuator and leakage ones.