Model extraction for fault isolation

This paper presents a simulation-based approach for fault isolation in complex dynamic systems. A machine learning technique is used to extract, from simulated data, models representing regularities in system behavior. A heuristic based on the degree of coverage of the model on the data is then applied to isolate faults. To test tolerance to incomplete models, our simulation model only requires I/O functions of relevant system processes that can be observed. We view our approach as an incremental filtering process, which is useful for diagnosis of large-scale systems. To illustrate the approach, we describe experiments in two examples including a well known three-tank system. Preliminary results show that, on the average, different types of faults at different locations such as a leaked tank and a blocked pipe can be isolated effectively more than 99% at a time. Results are promising but more in-depth study is required