研究基于时间序列数据挖掘的发动机功率损失故障诊断方法
Li Feng1,2, Le Liu1,3, Hongsheng Xu4,5
1School of Intelligent Connected Vehicle, Hubei University of Automotive Technology, Shiyan, 442002, China.
Scientific reports
|December 16, 2025
概括
本研究介绍了一种智能方法,用于通过时间序列数据挖掘来诊断商用车辆发动机功率损耗. 该方法通过分析车辆操作数据和采用机器学习和深度学习模型来提高远程诊断的准确性.
科学领域:
- 汽车工程 汽车工程
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 商用车辆的传统发动机功率损耗诊断是资源密集型的,严重依赖现场测试.
- 现有的方法面临的效率和可扩展性在现实世界车队管理的局限性.
研究的目的:
- 开发一种智能,数据驱动的用于商用车辆发动机功率损失故障的诊断方法.
- 通过远程在线诊断来克服传统现场测试的局限性.
- 利用时间序列数据挖掘和机器学习来加强故障识别.
主要方法:
- 收集和分析来自车载远程信息终端的现实世界运营数据.
- 确定了与发动机功率损失相关的关键特征:车辆速度,加速度和快门打开速度.
- 开发了一种双重框架策略:用于"带有驾驶员加速意图"数据的机器学习和用于"没有驾驶员加速意图"数据的深度学习.
主要成果:
- 拟议的智能诊断方法在识别发动机功率损失故障方面实现了高准确性和特异性.
- 双框架方法有效地区分了基于驾驶员意图的不同故障场景.
- 实验验证证了该方法的有效性和远程诊断的实际潜力.
结论:
- 该研究为商用车辆发动机功率损耗的远程在线诊断提供了一条可行的途径.
- 这些发现为汽车诊断的智能进步奠定了基础框架.
- 这种智能方法为传统诊断方法提供了更有效,更节省资源的替代方案.
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