通过双重注意力机制预测动力机械的剩余有用寿命的深度学习方法
Fan Wang1, Aihua Liu1,2, Chunyang Qu1
1School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本研究引入了一种先进的深度学习模型,用于预测动力机械的剩余使用寿命 (RUL). 新的双重注意力机制增强了特征提取,导致更准确的RUL预测,提高了系统可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 剩余使用寿命 (RUL) 预测对于动力机械的预测和健康管理 (PHM) 是至关重要的.
- 深度学习对RUL预测有希望,但与复杂的非线性降解模式作斗争.
- 现有的模型往往缺乏全面的特征提取能力,以适应动态运行条件.
研究的目的:
- 开发一种新的多功能聚变模型,用于在动力机械中增强RUL预测.
- 解决当前深度学习模型关于全面特征提取的局限性.
- 在不同的操作条件下提高RUL预测的准确性和可靠性.
主要方法:
- 一个双重注意力机制模型,集成卷积神经网络 (CNN) 和道注意力用于空间特征提取.
- 一个门反复单元 (GRU) 结合自我注意机制用于时间特征提取和集成.
- 用于最终RUL价值预测的回归分析.
主要成果:
- 与现有方法相比,拟议的模型在RUL预测方面表现优越.
- 对C-MAPSS数据集的验证证实了该模型的有效性.
- 双重注意力机制成功捕获了复杂的降解模式.
结论:
- 具有双重注意力的多功能融合模型显著提高了动力机械的RUL预测准确度.
- 这种方法为预测和健康管理 (PHM) 应用提供了更强大的解决方案.
- 该方法为预测性维护策略提供了可靠的基础.
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