Incorporating Prior Belief in the General Path Model: A Comparison of Information Sources

다국어 초록 (Multilingual Abstract)

The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. TheGPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failurethreshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognosticparameter observations are available. However, the parametric fit can suffer significantly when few data are available orthe data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conformto a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to includeprior information in the form of distributions of expected model parameters. This requires a number of run-to-failure caseswith tracked prognostic parameters; these data may not be readily available for many systems. Reliability information andstressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM.
This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacyof including different information sources on two data sets.

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