基于大数据和神经网络的直接驱动电液伺服性能特征预测
Juncheng Mi1, Jin Yu1, Guoqin Huang1
1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究开发了一种神经网络模型,以预测直驱电液压伺服的性能退化,这对于航空航天安全至关重要. 该模型准确地预测了门寿命,性能优于传统方法,满足了工业需求.
科学领域:
- 航空航天工程 航空航天工程
- 机械工程 机械工程
- 控制系统 控制系统
背景情况:
- 直驱电液压伺服对于航空航天控制系统的稳定性和安全性至关重要.
- 这些门的性能下降可能导致系统故障,事故和经济损失.
- 预测伺服寿命对于主动维护和操作安全至关重要.
研究的目的:
- 开发一个直接驱动电液伺服的性能降低的预测模型.
- 分析油污染和磨损等因素对伺服性能参数的影响.
- 与现有方法相比,验证模型的准确性和工业适用性.
主要方法:
- 在各种条件下进行了伺服性能降解实验,包括高油污染.
- 使用流量和位置传感器监测重叠,值和对称性等关键性能参数.
- 一个基于神经网络的预测模型被开发并使用实验数据进行训练.
主要成果:
- 实验数据显示,随着时间的推移,门部件的侵蚀和磨损增加,影响了性能参数.
- 性能参数表现出缓慢的退化趋势,在高油污染 (9级) 的情况下观察到更高的速度.
- 开发的神经网络模型与测试集相比,预测误差低于0.2%,显示出高准确度.
结论:
- 神经网络模型有效地预测了伺服的性能降低和寿命,满足了工业要求.
- 该模型在准确性和趋势预测方面优于指数级平滑和ARIMA等传统算法.
- 这项研究为航空航天和工业应用中伺服的AI驱动寿命预测模型提供了基础.
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