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.
Introduction Section One: Ending Business Surprises Chapter OneTurning Business Disasters Into Opportunities Chapter Two Identifying and Justifying the Right Real Time Information Section Two: Real Time in the Real World Chapter Three Surprise Event: Missing the Warning Chapter Four Suspected Event: Reporting Too Late Chapter Five Surmounted Events: Getting it Right Section Three: From Real Time Opportunity Detection to the Real Time Enterprise Chapter Six Deploying Real Time Opportunity Detection Across the Enterprise Chapter Seven Solving the Challenges of Deploying Real Time Opportunity Chapter Eight The Future in a Real Time World Conclusion: The Time is Now