Dynamic Bayesian Networks for Prognosis

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Gregory Bartram Sankaran Mahadevan

Abstract

In this paper, a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN) is proposed. Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats from various sources and give a probabilistic representation of a system. Further, DBNs provide a platform naturally suited for seamless integration of diagnosis, uncertainty quantification, and prediction. In the proposed methodology, a DBN is used for online diagnosis via particle filtering, providing a current estimate of the joint distribution over the system variables. From this state estimate, future states of the system are predicted using the DBN and sequential Monte Carlo sampling. Prediction in this manner provides the necessary information to estimate the distribution of remaining use life (RUL). The DBN-based recursive prediction procedure may be used to estimate the system state between available measurements, when filtering is not possible. The prognosis procedure, which is system specific, is validated using a suite of offline hierarchical metrics. The prognosis methodology is demonstrated on a hydraulic actuator subject to a progressive seal wear that results in internal leakage between the chambers of the actuator.

How to Cite

Bartram, G. ., & Mahadevan, S. . (2013). Dynamic Bayesian Networks for Prognosis. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2246
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Keywords

diagnosis, particle filtering, prognosis, Dynamic Bayesian Network, hydraulic actuator

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Technical Papers

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