Temporal versus Spatial Observability in Model-Based Diagnosis

Accurate fault diagnosis is a crucial success factor in achieving system dependability. The unambiguity of a diagnosis is critically dependent on the number of observations available for the inference process. Observability, therefore, significantly determines diagnostic quality. In this paper we introduce the notion of a temporal and a spatial dimension to observability of Model-Based Diagnosis, and study their impact on diagnostic quality. We use uncertainty, measured as the expected entropy of a diagnosis after observation, as quality metric. Empirical results confirm that for both dimensions an increase in observability always leads to a reduction of expected, diagnostic entropy. However, the temporal and the spatial reduction factors differ in that they decrease and increase respectively with increasing observability. The results from this study are useful for performing practical trade-offs between additional sensor placements and longer measurement horizons.