On-line Fault Detection and Isolation of Nonlinear Systems

This paper describes an on-line fault detection scheme for a class of nonlinear dynamic systems with modelling uncertainty and inaccessible states. Only the inputs and outputs of the system can be measured. The faults are assumed to be functions of the state, instead of the output, and the input of the system. A nonlinear on-line approximator using dynamic recurrent neural network (DRNN) is utilised to monitor the faults in the system. The construction and the learning algorithm of the on-line approximator are presented. The stability, robustness and sensitivity of the fault detection scheme under certain assumptions are analysed. An example demonstrates the efficiency of the proposed fault detection scheme.