A generic Bayesian approach for model-based prognostics with Laplace approximation

Liang Xu, Pingfeng Wang

Research output: Contribution to conferencePaperpeer-review


This paper presents a generic Bayesian framework using Laplace approximation for model-based remaining useful life prognosis. The developed generic Bayesian prognosis approach models and updates remaining useful life distributions by incorporating timely evolving sensory data using a general Bayesian inference mechanism and employs an efficient Bayesian updating approach using an Laplace approach (LA) method. The developed Bayesian prognosis approach eliminates the dependency of evolutionary updating process on a selection of distribution types for the parameters for a given system degradation model. Furthermore, with the developed LA method, the Bayesian updating process can be carried out efficiently which makes the proposed approach possible for real-time prognosis applications. The proposed Bayesian prognosis methodology is generally applicable for different degradation models without prior distribution constraints as faced by conjugate or semi-conjugate Bayesian inference models. Two practical prognosis applications are employed in this study to demonstrate the efficacy of the proposed Bayesian prognosis methodology.

Original languageEnglish (US)
Number of pages10
StatePublished - 2013
Externally publishedYes
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: May 18 2013May 22 2013


OtherIIE Annual Conference and Expo 2013
Country/TerritoryPuerto Rico
CitySan Juan


  • Bayesian inference
  • Prognosis
  • Reliability
  • Remaining useful life

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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