On handling dependent evidence and multiple faults in knowledge fusion for engine health management

Diagnostic architectures that fuse outputs from multiple algorithms are described as knowledge fusion or evidence aggregation. Knowledge fusion using a statistical framework such as Dempster-Shafer (D-S) has been used in the context of engine health management. Fundamental assumptions made by this approach include the notion of independent evidence and single fault. In most real world systems, these assumptions are rarely satisfied. Relaxing the single fault assumption in D-S based knowledge fusion involves working with a hyper-power set of the frame of discernment. Computational complexity limits the practical use of such extension. In this paper, we introduce the notion of mutually exclusive diagnostic subsets. In our approach, elements of the frame of discernment are subsets of faults that cannot be mistaken for each other, rather than failure modes. These subsets are derived using a systematic analysis of connectivity and causal relationship between various components within the system. Specifically, we employ a special form of reachability analysis to derive such subsets. The theory of D-S can be extended to handle dependent evidence for simple and separable belief functions. However, in the real world the conclusions of diagnostic algorithms might not take the form of simple or separable belief functions. In this paper, we present a formal definition of algorithm dependency based on three metrics: the underlying technique an algorithm is using, the sensors it is using, and the feature of the sensor that the algorithm is using. With this formal definition, we partition evidence into highly dependent, weakly dependent and independent evidence. We present examples from a Honeywell auxiliary power unit to illustrate our modified D-S method of evidence aggregation.