Applying Outbreak Detection Algorithms to Prognostics (2007)

Artur Dubrawski, Michael Baysek, Shannon Mikus, Charles McDaniel, Bradley Mowry, Laurel Moyer, John Ostlund, Norman Sondheimer, Timothy Stewart

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Heath of Equipment

Abstract

Fleet maintenance and supply management systems are
challenged to increase the availability and reliability of
equipment. Prognostics can help. This paper examines the
utility of selected statistical data mining algorithms,
originally developed for bio-surveillance applications, in
achieving fleet prognostics. Preliminary experimental
evaluation suggests that it is possible, useful and practical to
apply such algorithms to rapidly detect emerging patterns of
systematic failures of equipment or support processes, and
to continuously monitor relevant data for indications of
specific types of failures. The key remaining technical
challenge is to tame down a potentially large number of
plausible pattern detections without compromising high
detectability rates. The key practical consequences to
maintenance and supply managers include the ability to be
notified about emergence of a possible problem
substantially earlier than before, the ability to routinely
screen incoming data for indications of problems of all
conceivable types even if their number is very large, and the
ability to pragmatically prioritize investigative efforts
according to the statistical significance of the detections.

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Approximate BibTeX Entry

@inproceedings{Dubrawski_etal_AAAIFall2007,
    Month = {November},
    Year = {2007},
    Address = {Arlington, VA,},
    Booktitle = {AAAI Fall Symposium on Artificial Intelligence in Prognostics},
    Author = { Artur Dubrawski, Michael Baysek, Shannon Mikus, Charles McDaniel, Bradley Mowry, Laurel Moyer, John Ostlund, Norman Sondheimer, Timothy Stewart },
    Title = {Applying Outbreak Detection Algorithms to Prognostics}
}