A hierarchical Bayesian regression framework for enabling online reliability estimation and condition-based maintenance through accelerated testing

Leonardo Leoni, Filippo De Carlo*, Mohammad Mahdi Abaei, Ahmad BahooToroody

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Thanks to the advances in the Internet of Things (IoT), Condition-based Maintenance (CBM) has progressively become one of the most renowned strategies to mitigate the risk arising from failures. Within any CBM framework, non-linear correlation among data and variability of condition monitoring data sources are among the main reasons that lead to a complex estimation of Reliability Indicators (RIs). Indeed, most classic approaches fail to fully consider these aspects. This work presents a novel methodology that employs Accelerated Life Testing (ALT) as multiple sources of data to define the impact of relevant PVs on RIs, and subsequently, plan maintenance actions through an online reliability estimation. For this purpose, a Generalized Linear Model (GLM) is exploited to model the relationship between PVs and an RI, while a Hierarchical Bayesian Regression (HBR) is implemented to estimate the parameters of the GLM. The HBR can deal with the aforementioned uncertainties, allowing to get a better explanation of the correlation of PVs. We considered a numerical example that exploits five distinct operating conditions for ALT as a case study. The developed methodology provides asset managers a solid tool to estimate online reliability and plan maintenance actions as soon as a given condition is reached.

Original languageEnglish
Article number103645
Number of pages13
JournalComputers in Industry
Volume139
DOIs
Publication statusPublished - 2022

Keywords

  • Condition monitoring
  • Condition-based maintenance
  • Generalized linear model
  • Hierarchical Bayesian regression
  • Online reliability estimation

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