| dc.description.abstract | Fault diagnosis approaches for nonlinear real-world systems play a very important role in maintaining dependable, robust operations of safety-critical systems like aircraft, automobiles, power plants and planetary rovers. They require online tracking functions to monitor system behavior and ensure system operations remain within specified safety limits. It is important that such methods are robust to uncertainties, such as modeling errors, disturbance and measurement noise. In this thesis, we employ a temporal Bayesian technique called Dynamic Bayesian Networks (DBNs) to model nonlinear dynamic systems for uncertain probabilistic reasoning in diagnosis application domains. Within the DBN framework, we develop the modeling scheme, model construction process, and the use of the models to build diagnostic models for online diagnosis. This thesis also performs a preliminary comparison of two particle filter algorithms: generic particle filters (GPF) and auxiliary particle filter (APF). These are commonly used for tracking and estimating the true system behavior. Our approach to diagnosis includes a DBN model based diagnosis framework combining qualitative TRANSCEND scheme and quantitative methods for refining the fault isolation, and using parameter estimation techniques to provide more precise estimates of fault hypotheses. As a proof of concept, we apply this DBN based diagnosis scheme to the Reverse Osmosis (RO) subsystem of the Advanced Water Recovery System (AWRS). Performance of the two particle filter algorithms are compared based on a number of fault scenarios and different levels of noise as well. The results show our DBN-based scheme is effective for fault isolation and identification of complex nonlinear systems. | |
