智能故障诊断系统用于高速列车的运行轮
Shuai Yang1, Guoliang Gao1, Ziyang Wang1
1School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了铁路运输列车运行轮故障诊断的TimesNet,达到91.7%的准确性. 该方法可实现实时监控,增强复杂机械系统的预测性维护.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的铁路运输故障诊断依赖于手动检查,这不足以满足复杂环境中的实时,高精度需求.
- 现有的深度学习方法在多变量时间序列数据的特征提取和计算成本方面扎.
研究的目的:
- 引入和验证TimesNet框架,用于对轨道交通列车行车设备进行智能故障诊断.
- 增强机械系统中时间序列数据的时空表示和特征提取能力.
主要方法:
- 使用了时代网,采用了多周期分解策略和1D到2D数据重塑.
- 在可变条件下验证了高速列车轴承和变速箱故障数据集的模型.
主要成果:
- 在高速列车轴承故障数据集上实现了91.7%的诊断准确度.
- 在70.3 ± 5.8ms内证明实时推断,满足<100ms的要求.
- 2D重塑增强了在多变量时间序列数据中的周期结构的捕获.
结论:
- 时代网为火车运行轮的智能故障诊断提供了一个新的范式,提高了准确性和实时能力.
- 该研究为铁路运输系统的先进预测性维护技术奠定了基础.
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