Probabilistic Prognosis Using Dynamic Bayesian Networks

Gregory Bartram and Sankaran Mahadevan
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Publication Issue: 
Special Issue Uncertainty in PHM
Submission Type: 
Full Paper
ijphm_15_002.pdf2.13 MBJune 10, 2015 - 8:41am

This paper proposes a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN). 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 the system state. 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. The information available in the state estimate also helps to quantify the uncertainty in diagnosis. Next, based on this probabilistic state estimate, future states of the system are predicted using the DBN and sequential or recursive Monte Carlo sampling. Prediction in this manner provides the necessary information to estimate the distribution of remaining use life (RUL). 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.

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Submission Keywords: 
Dynamic Bayesian Network
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
Uncertainty Quantification and Management in PHM
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