Predictive modeling of vehicle failures with hierarchical Bayesian methods for workforce planning

Abstract

Vehicles that operate under demanding conditions need an understanding of failures to ensure reliability and take appropriate actions. To address this, a statistical framework is developed for modeling failure times using real-world operational data. The approach employs Bayesian Generalized Linear Mixed Models to capture unit and vehicle-level effects, and intervention effects. A sequential simulation framework models temporal dependencies and generates multi-step failure predictions with full uncertainty quantification. The proposed model and simulation approach are evaluated to demonstrate both calibration and predictive performance. Additionally, the work shows how predictive outputs can inform decision-making by deriving new system-level metrics and assessing their reliability. Finally, the results are applied in a representative sequential decision-making problem on workforce planning for repair actions.