An online approximator-based fault detection framework for nonlinear discrete-time systems

In this paper, a fault detection scheme is developed for nonlinear discrete time systems. The changes in the system dynamics due to incipient failures are modeled as a nonlinear function of state and input variables while the time profile of the failures is assumed to be exponentially developing. The fault is detected by monitoring the system and is approximated by using online approximators. A stable adaptation law in discrete-time is developed in order to characterize the faults. The robustness of the diagnosis scheme is shown by extensive mathematical analysis and simulation results.