Predictive maintenance optimization for industrial equipment via reliable prognosis and risk-aware reinforcement learning

Predictive maintenance (PdM) based on Remaining Useful Life (RUL) prediction plays a crucial role in improving performance and reducing lifecycle costs of industrial equipment. This study proposes an intelligent PdM framework that integrates a RUL prediction model based on probabilistic neural network with a distributional reinforcement learning agent based on QR-DQN. In the first stage, the RUL prediction model is developed to process sensor data to generate accurate RUL predictions, quantify predictive uncertainty, and estimate the probability of failure within a given horizon. Building on the health condition assessment, the QR-DQN agent learns the distribution of long-term maintenance returns and makes sequential decisions among multiple actions. By adopting risk-sensitive decision rules, the agent explicitly accounts for uncertainty and failure risk, achieving a balance between safety, cost efficiency, and timeliness of interventions. Experimental evaluations on complex system degradation demonstrate that the proposed intelligent PdM outperforms conventional baselines by reducing catastrophic failures, optimizing maintenance schedules, and improving overall reliability.