改进了RAKEL对高速列车引变压器的故障诊断方法
Man Li1,2,3, Xinyi Zhou1,2, Siyao Qin2
1State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究引入了一种改进的RAKEL算法,用于诊断高速列车引变压器故障,使用维护数据来提高准确性. 这种新方法提高了故障识别,有助于及时维护和提高火车安全.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 拉力变压器故障在高速列车中很常见,影响安全运行.
- 目前的故障诊断严重依赖于手工经验,这种经验效率低下,容易出现错误.
研究的目的:
- 开发一个改进的RAKEL (Random k-Labelsets) 算法,用于准确诊断高速列车引变压器的故障.
- 为了提高诊断能力,利用历史维护数据.
主要方法:
- 从维护记录中的特定监控值构建实例向量.
- 使用Relief算法提取相关故障并改善RAKEL中的k-labelset选择.
- 应用数据和错误之间的关联规则用于识别.
主要成果:
- 改进的RAKEL算法在故障诊断的评估指标中显示出显著的改进.
- 超过了其他多标签分类算法,如二进制相关性 (BR) 和校准标签排名 (CLR).
结论:
- 拟议的方法提供了一种更有效的方法来诊断高速列车的引变压器故障.
- 这可以帮助工程师及时进行维护,从而提高运营安全.
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