直升机轮轴发动机的神经网络系统用于监测传感器故障
Serhii Vladov1, Łukasz Ścisło2, Nina Szczepanik-Ścisło3,4
1Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
使用LSTM和GRU的新混合神经网络系统有效地监控直升机轮轴发动机传感器以检测异常. 这种先进的系统实现了高精度,并缩短了训练时间,改善了整体发动机健康监测.
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
- 机器学习 机器学习
背景情况:
- 直升机轮轴发动机从众多传感器生成复杂的顺序数据.
- 准确及时检测异常对于确保发动机安全和可靠性至关重要.
- 现有的监控系统在处理时间序列数据和识别微妙异常时可能面临挑战.
研究的目的:
- 开发和评估一种新的神经网络系统,用于加强直升机轮轴发动机中的传感器监控.
- 提高关键发动机组件异常检测的准确性和效率.
- 为了利用混合循环神经网络架构来进行强大的顺序数据分析.
主要方法:
- 开发了一种混合神经网络架构,结合了长短期内存 (LSTM) 和门式循环单元 (GRU) 层.
- 在SensorFailClean和SensorFailNorm模块中实施了自适应性分离和量化技术,以提高数据质量.
- 使用了一种结合时间规范化和联合优化方法 (SGD与RMSProp) 的训练算法.
主要成果:
- 经过200个训练时代,开发的系统实现了高异常检测准确率99.327%,达到200个训练时代.
- 训练时间大幅缩短至4分13秒,精度为0.993.
- 与替代方法相比,该系统表现出卓越的性能,准确度为0.993,而0.981和0.982.
结论:
- 混合LSTM-GRU神经网络系统为监控直升机轮轴发动机传感器提供了有效的解决方案.
- 该系统在异常检测和故障识别方面表现出高精度,最大限度地减少遗漏.
- 拟议的方法通过改进数据处理和预测能力,在发动机健康监测方面取得了重大进展.
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