Model-based sensor and actuator fault detection and isolation

This work concerns the development of an analytical redundancy-based approach for detecting and isolating sensor, actuator, and component (i.e., plant) faults in complex dynamical systems, such as aircraft and spacecraft. The method is based on the use of constrained Kalman filters, which are able to detect and isolate such faults by exploiting functional relationships that exist among various subsets of available actuator input and sensor output data. A statistical change detection technique based on a modification of the standard generalized likelihood ratio statistic is used to detect faults in real time. The feasibility and efficacy of the approach is demonstrated through simulation in the context of a nonlinear jet engine control system.