Bayesian Hierarchical Models for aerospace gas turbine engine prognostics

作者：

Highlights：

• Data-driven approach is suitable for gas turbine engine prognostics.

• Bayesian methods produce predictive results within well defined uncertainty bounds.

• Bayesian Hierarchical Model (BHM) uses optimally degradation data for prognostics.

• This integrates effectively multiple unit data to address realistic prognostic challenges.

摘要

•Data-driven approach is suitable for gas turbine engine prognostics.•Bayesian methods produce predictive results within well defined uncertainty bounds.•Bayesian Hierarchical Model (BHM) uses optimally degradation data for prognostics.•This integrates effectively multiple unit data to address realistic prognostic challenges.

论文关键词：Condition-based maintenance,Prognostics,Gas turbine engine,Bayesian Hierarchical Model

论文评审过程：Available online 23 August 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.08.007