A Novel Efficient Method for Conflicts Set Generation for Model-Based Diagnosis

In this paper we present a new efficient algorithmic method for generating the conflicts set for model-based diagnosis. Our new method combines the strength of the two different approaches proposed in the literature, that is, the Fault Detection and Isolation (FDI), which is based on automatic control theory and statistical decision theory, and the other one, known as DX, which is based on artificial intelligence techniques. The first building block in our method is a new efficient algorithm for generation of the complete set of Analytical redundancy Relations (ARRs) for the system in an implicit form. For the diagnosis, our method first performs (similar to DX approaches) a system simulation to calculate the expected values of the measurements. Any discrepancy, i.e., the difference between expected and actual value of measurement, would trigger our diagnosis process. To this end, only those ARRs which involve the measurement with discrepancy are checked for consistency which lead a to a significant reduction in the number of consistency checks usually performed by DX approaches. We demonstrate the efficiency of our new method by its application to several synthetic systems and compare it with that of GDE.