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tk-yasuno / dql-multi-equipments-cbm

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Multi-Equipment CBM system using QR-DQN with advanced probability distribution analysis. Coordinated maintenance decision-making for 4 industrial equipment units with realistic anomaly rates (1.9-2.2%), comprehensive risk analysis (VaR/CVaR), and 51-quantile distribution visualization.

reinforcement-learning risk-analysis uncertainty-estimation value-at-risk deep-q-learning predictive-maintenance qr-dqn distributional-rl condition-based-maintenance multi-agent-rl cvar prognostics-health-management disaster-resilience equipment-maintenance infrastructure-prognostics
  • Updated Dec 21, 2025
  • Python

tk-yasuno / dql-aged-equipment-cbm

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Aging-Aware Condition-Based Maintenance System using Deep Q-Learning. This project implements a Condition-Based Maintenance (CBM) system that considers equipment aging (deterioration) using Deep Q-Learning (DQN).

reinforcement-learning dqn deep-q-learning predictive-maintenance deterioration-modeling condition-based-maintenance prognostics-health-management aging-model disaster-resilience equipment-maintenance infrastructure-prognostics aging-aware-rl
  • Updated Dec 23, 2025
  • Python

tk-yasuno / dql-equipment-cbm

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A Reinforcement Learning MVP (Minimum Viable Product) for Condition-Based Maintenance (CBM) using industrial equipment temperature sensor data. This project implements a sophisticated QR-DQN (Quantile Regression Deep Q-Network) agent to learn optimal maintenance policies balancing risk mitigation and cost minimization.

reinforcement-learning temperature-sensor sensor-data deep-q-learning quantile-regression cost-optimization predictive-maintenance risk-mitigation qr-dqn distributional-rl condition-based-maintenance prognostics-health-management disaster-resilience equipment-maintenance infrastructure-prognostics risk-sensitive-rl
  • Updated Dec 21, 2025
  • Python

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