Delay-aware predictive maintenance control for multi-equipment systems using approximate Bayesian neural reliability networks

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Abstract

This study proposes a delay-aware predictive maintenance control framework for multiequipment systems operating under dynamic and uncertain industrial environments. The proposed framework integrates a multilayer neural reliability prediction model, an approximate Bayesian reliability updating mechanism, and a rolling-horizon maintenance optimization model to support maintenance decision-making under delayed and asynchronous industrial information. Unlike conventional predictive maintenance approaches that rely primarily on instantaneous observations or static reliability estimation, the proposed framework explicitly models time-varying communication delays, degradation evolution, imperfect maintenance effects, and dynamic state propagation within a closed-loop maintenance control architecture. The proposed methodology first estimates machine failure probabilities using a multilayer neural network trained on heterogeneous operational and degradation-related variables. The predicted reliability is subsequently refined through approximate Bayesian filtering by integrating historical posterior information and delayed observations. Based on the updated reliability trajectories, a rolling-horizon genetic algorithm determines preventive maintenance schedules while considering maintenance capacity, workforce availability, and operational constraints. The optimization objective minimizes the total expected maintenance cost, including preventive maintenance, corrective maintenance, downtime, and reliability penalty costs. Experimental results demonstrate that the proposed framework provides competitive probability prediction performance while achieving superior maintenance cost reduction and improvements in reliability under delayed operating conditions.

Keywords

delay-aware predictive maintenance / approximate Bayesian reliability updating / neural reliability prediction / preventive maintenance scheduling / multiequipment systems / smart manufacturing

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