Efficient Model Based Diagnosis
This work addresses efficient fault diagnosis for systems with causal components, offering incremental improvements in computational efficiency for specific connectivity conditions.
The paper tackles the problem of diagnosing faults in systems with causal component relations by proposing a two-step diagnostic process that identifies likely broken components and selects informative probing points, achieving worst-case O(n^2) time complexity but linear complexity for low-connectivity systems.
In this paper an efficient model based diagnostic process is described for systems whose components possess a causal relation between their inputs and their outputs. In this diagnostic process, firstly, a set of focuses on likely broken components is determined. Secondly, for each focus the most informative probing point within the focus can be determined. Both these steps of the diagnostic process have a worst case time complexity of ${\cal O}(n^2)$ where $n$ is the number of components. If the connectivity of the components is low, however, the diagnostic process shows a linear time complexity. It is also shown how the diagnostic process described can be applied in dynamic systems and systems containing loops. When diagnosing dynamic systems it is possible to choose between detecting intermitting faults or to improve the diagnostic precision by assuming non-intermittency.