On-line Approximation Methods for Robust Fault Detection

Abstract

Fault diagnosis plays an important role in the operation of modern engineering plants. The design and analysis of fault diagnosis architectures using the model-based analytical redundancy approach has received considerable attention during the last two decades. One of the key issues in the design of such fault detection architectures is the effect of modeling uncertainties on their performance. In this paper, we propose a methodology to detect and diagnose faults in nonlinear dynamic systems with modeling uncertainties. The main idea behind this approach is to monitor the plant for any off-nominal system behavior due to faults utilizing a nonlinear on-line approximator with adjustable parameters. Learning algorithms based on Lyapunov's method are described and analyzed for robustness, sensitivity and stability.