An On-Line Approximation Approach to Fault Monitoring, Diagnosis and Accommodation

The detection, diagnosis, and accommodation of system failures or degradations is becoming increasingly more important in modern engineering problems. This paper presents a general framework for constructing automated fault diagnosis and accommodation architectures using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. Changes in the system dynamics are monitored by an on-line approximation model, which is used not only for detecting but also for accommodating failures. A systematic procedure for constructing nonlinear estimation algorithms and stable learning schemes is developed and illustrated by a simulation example.