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

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