TWO NEURAL NET-LEARNING METHODS FOR MODEL BASED FAULT DETECTION

Residual generation is an essential part of model-based fault detection schemes. For nonlinear systems, the task of residual generation is sometimes complicated by the size of the problem, or by the lack of a suitable model from where the residual can be generated. This paper develops and implements neural-networks based system identification techniques for nonlinear systems with the specific goal of residual generation for fault detection purposes. Two NN structures were investigated in this paper: a new structure of partially connected neural networks (PCNN), and a conventional, fully connected neural network (FCNN). The two approaches are tested on a Boeing 747 aircraft model. Results of computer experiments are reported. Performance comparisons of the two neural networks are presented.