基于空间和时间改进的铁路交通列车逆变器的温度预测方法的研究 变压器
Yanhong Feng1, Yinan Zhai2, Fu Wang3
1School of Information Science & Engineering, Dalian Ocean University, Dalian, China.
Scientific reports
|September 30, 2025
概括
一个新的混合框架使用增强的数据多样性和时空分析预测了铁路运输中的逆变器过热. 这种智能系统可以改善故障预防,使铁路运行更安全,更有效.
科学领域:
- 铁路交通工程 铁路交通工程
- 人工智能的人工智能是人工智能.
- 预测性维护是指预测性维护.
背景情况:
- 逆变器过热是铁路运输系统的一个关键故障点.
- 挑战包括稀疏的低压数据和高维特征,阻碍准确的预测.
- 现有的方法与数据稀缺性和复杂性作斗争.
研究的目的:
- 开发一种混合预测框架,用于在铁路运输中准确预测逆变器温度.
- 使用先进的人工智能技术解决数据稀疏性和高维度问题.
- 通过智能故障预防,提高铁路运营的安全性和效率.
主要方法:
- 使用随机掩盖双 DCGAN (RTDG) 进行低压数据增强.
- 使用高斯马尔科夫随机场 (GMRF) 进行维度缩小和关键变量识别.
- 开发了一个增强的变压器架构 (STTr),集成状态空间建模和时间正常化,以捕获时空依赖.
- 使用加权堆叠策略并根据真实世界铁路运输数据验证的合元件.
主要成果:
- 拟议的混合模型在逆变器温度预测方面明显优于传统方法.
- 与单个模型相比,实现了4.93%的改进,与非增强培训相比,获得了9.73%的收益.
- 在处理稀疏数据和复杂特征方面表现出有效性.
结论:
- 开发的混合框架为铁路运输中逆变器过热预测提供了强大的解决方案.
- 支持智能故障预防,有助于提高安全性和运营效率.
- 为维护现代铁路系统的完整性提供了有价值的工具.
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