健康状况预测与强化学习用于预测性维护
Anastasis Aglogallos1, Alexandros Bousdekis1, Stefanos Kontos1
1Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece.
Frontiers in artificial intelligence
|January 28, 2026
概括
强化学习 (RL) 为预测性维护提供了传统机器学习的强大替代方案,在条件不断变化的场景中表现出色. 靠近政策优化 (PPO) 和软行为者批判 (SAC) 证明了CNC机床磨损预测中最有效和最稳定的性能.
科学领域:
- 制造业 制造技术 制造技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 预测性维护对于工业4.0至关重要,但传统方法在标记数据和适应性方面扎.
- 强化学习 (RL) 通过互动学习最佳策略,绕过标记的数据需求并处理设备退化动态.
研究的目的:
- 评估在制造业中用于预测性维护的无模型RL算法.
- 在不同环境中比较近接政策优化 (PPO),优势行为者-关键 (A2C),深度决定性政策梯度 (DDPG) 和软行为者-关键 (SAC) 的表现.
主要方法:
- 制定了CNC机床磨损预测作为马尔科夫决策过程 (MDP).
- 实现并测试了四个无模型的RL算法:PPO,A2C,DPG和SAC.
- 在四个自定义环境中验证了性能,分析了学习动态,融合和概括.
主要成果:
- PPO和SAC表现出最稳定和最有效的表现.
- 在结构化环境中,SAC表现出色,而PPO表现出强大的泛化能力.
- A2C显示了持续的长期学习;由于探索有限,DDPG表现不佳.
结论:
- RL显示了先进的预测性维护应用的巨大潜力.
- 算法选择应与特定的环境特征和奖励结构保持一致,以获得最佳的结果.
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