Fault Diagnostics and Supervised Testing: How Fault Diagnostic Tools can be Proactive?

M. Najafi, D.M. Auslander, P.L. Bartlett, and P. Haves (USA)


Fault Diagnosis, Machine Learning, Bayesian Networks,, HVAC Systems


The topic of fault detection and diagnostics (FDD) is studied from the perspective of proactive testing. Unlike most research focus in the diagnosis area in which system outputs are analyzed for diagnosis purposes, in this paper the focus is on the other side of the problem: manipulating system inputs for better diagnosis reasoning. In other words, the question of how diagnostic mechanisms can direct system inputs for better diagnosis analysis is addressed here. It is shown how the problem can be formulated as decision making problem coupled with a Bayesian Network based diagnostic mechanism. The developed mechanism is applied to the problem of supervised testing in HVAC systems.

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