一种基于石油温度预测模型的故障检测方法,使用在直升机变速箱中改进的深度决定性政策梯度算法
Lei Wei1,2,3, Zhe Cheng1,2, Junsheng Cheng3
1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种先进的AI模型,用于直升机变速箱油温预测. 新方法提高了故障检测的准确性,并缩短了检测时间,提高了运营安全.
科学领域:
- 航空航天工程 航空航天工程
- 人工智能的人工智能
- 机械工程 机械工程
背景情况:
- 直升机变速箱的安全性对于运行至关重要.
- 变速箱油温是变速箱健康状况的一个关键指标.
- 准确的油温预测对于可靠的故障检测至关重要.
研究的目的:
- 开发一个准确的直升机轮箱油温预测模型.
- 提高故障检测系统的可靠性和效率.
- 为了提高直升机操作的整体安全性.
主要方法:
- 一个改进的深度决定性政策梯度算法与CNN-LSTM学习器.
- 一个奖励激励功能加速培训和模型稳定.
- 一种可变方差探索策略,用于有效的状态空间探索.
- 一个多关键网络结构,用于准确的Q值估计.
- 核密度估计 (KDE) 用于在EWMA处理后确定故障值.
主要成果:
- 拟议的模型证明了变速箱油温的更高的预测准确性.
- 该模型显著降低了故障检测时间成本.
- 整合CNN-LSTM有效地捕捉了复杂的温度-工作条件关系.
- 多关键网络可以提高Q值估计的准确性.
结论:
- 开发的AI模型在直升机变速箱健康监测方面取得了重大进展.
- 拟议的预测方法提高了故障检测的速度和准确性.
- 这种方法有助于通过预测性维护提高直升机的操作安全性.
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