BAYESIAN UPDATING OF RAM MODELS FOR DYNAMIC AVAILABILITY FORECASTING UNDER REALISTIC DOWNTIME CONSTRAINTS

Abstract

This paper presents a Bayesian framework for the dynamic updating of Reliability, Availability, and Maintainability (RAM) models applied to complex industrial systems operating under realistic downtime constraints. Conventional RAM analysis relies on maximum likelihood estimation (MLE) from historical failure data, which frequently fails to account for maintenance window restrictions, crew availability queues, spare-parts lead times, and other operational downtime penalties that systematically inflate effective repair durations in practice. The proposed methodology integrates conjugate Bayesian inference — implemented through Markov Chain Monte Carlo (MCMC) sampling — with an explicit downtime penalty function to revise MTBF and MTTR posterior distributions as field data accumulate. Validation is performed on three industrial system types (rotating pump, reciprocating compressor, and gas turbine) over a 24-month monitoring horizon. Results demonstrate that the Bayesian-updated model reduces forecast RMSE by 66% relative to classical MLE, improves predicted availability by up to 4.3 percentage points, and correctly captures the widening uncertainty bounds associated with planned maintenance windows and resource constraints. The framework is directly implementable within existing CMMS environments and provides actionable inputs for proactive maintenance scheduling and asset lifecycle optimisation.