基于深度强化学习的滚动轴承剩余使用寿命预测方法
Yipeng Wang1,2, Yonghua Li1, Hang Lu3,4
1College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116000, China.
The Review of scientific instruments
|September 16, 2024
概括
本研究引入了一种新的深度强化学习 (DRL) 方法,用于预测工业系统的剩余使用寿命 (RUL). 该方法结合了CNN-BiLSTM和DDPG,准确预测设备的寿命,性能优于现有技术.
科学领域:
- 工业工程 工业工程 工业工程
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 预测剩余使用寿命 (RUL) 对工业健康管理和安全至关重要.
- 目前的RUL预测方法主要使用深度学习 (DL),对深度强化学习 (DRL) 的探索有限.
研究的目的:
- 引入一种基于DRL的新方法来预测RUL,增强当前的方法.
- 将DL特征提取与DLR决策集成在一起,以改进RUL估计.
主要方法:
- 一种混合的卷积神经网络-双向长期短期记忆网络 (CNN-BiLSTM) 用于特征提取.
- 深度决定性政策梯度 (DDPG) 算法在DRL框架内用于预测RUL.
- RUL预测任务以马尔科夫决策过程 (MDP) 为模型.
主要成果:
- 基于DRL的方法在IMS载有数据集的现有方法相比显示出更高的性能.
- 该模型实现了更低的根平均平方误差 (MSE) 和提高了RUL预测的准确性.
- 预测的RUL值显示与实际设备寿命更接近.
结论:
- 拟议的DLR方法通过整合先进的DL和DLR技术,有效地预测RUL.
- 这种方法为实时设备健康监测和预测性维护提供了有希望的进步.
- 该研究强调了DRL在解决当前RUL预测策略的局限性方面的潜力.
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