Continuous Time Bayesian Networks in Prognosis and Health Management of Centrifugal Pumps

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Published Sep 22, 2019
Tyler Forrester Mark Harris Jacob Senecal John Sheppard

Abstract

This paper presents a novel method for performing risk-based prognosis and health management (rPHM) on centrifugal pumps. We present the rPHM framework and apply common modeling tools used in reliability and testability analysis---dependency (D) matrices and fault tree analysis---as a basis for constructing an underlying predictive model. We then introduce the mathematics of the Continuous Time Bayesian Network (CTBN), which is a probabilistic graphical model based on a factored Markov process that is designed to capture system evolution through time, and we explain how to apply a CTBN derived from D-matrices and fault trees to consider the impact of a set of faults common to centrifugal pumps on emerging hazards in the pump system. We demonstrate the utility of using CTBNs for rPHM analysis with two experiments showing the descriptive power of our modeling approach.

How to Cite

Forrester, T., Harris, M., Senecal, J., & Sheppard, J. (2019). Continuous Time Bayesian Networks in Prognosis and Health Management of Centrifugal Pumps. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.778
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Keywords

Continuous Time Bayesian Networks, Centrifugal Pumps

Section
Technical Papers