Degradation Modeling and Maintenance Decisions Based on Bayesian Belief Networks

Abstract

A variety of data-driven models focused on remaining lifetime prediction have been developed under condition-based monitoring framework. These models either assume an analytical formula for the underlying degradation path is known, or the number of degradation states could be determined subjectively. This paper proposes an adaptive discrete-state model to estimate system remaining lifetime based on Bayesian Belief Network (BBN) theory. The model consists of three steps: degradation state identification, degradation state characterization, and remaining life prediction. Our approach does not require an explicit distribution function to characterize the fault evolutionary process. Because the BBN model leverages the validity measures to determine the optimum state number, it avoids the state identification errors under limited feature data. The performance of the BBN model is validated and verified by actual and simulated bearing life data. Numerical examples show that the Bayesian degradation model outperforms a time-based maintenance policy both in cost and reliability.