在缺失数据条件下,基于深度PCA和MSPCA对高速列车引系统的故障诊断
Yunkai Wu1, Yu Tian1, Yang Zhou2
1College of Automation, Jiangsu University of Science and Technology, Zhenjiang, 212100, PR China.
ISA transactions
|November 13, 2025
概括
这项研究引入了一种新的方法来修复高速列车中缺少的传感器数据,改善在具有挑战性的操作条件下早期故障检测和隔离.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
背景情况:
- 高速列车的引系统面临着高温和振动的复杂环境.
- 由于系统复杂性和缺少传感器数据,初始故障难以诊断.
研究的目的:
- 为不断缺失传感器信号提出创新的数据归算方法.
- 开发一个实时初始故障诊断框架,以提高准确性.
- 为了在高速列车引系统中实现精确的故障隔离.
主要方法:
- 一种新的数据归算方法,结合了内核函数和修改的阿基马 (Makima) 插值.
- 一个实时故障诊断框架,集成深度主要组件分析 (Deep-PCA) 和多尺度主要组件分析 (MSPCA).
- 连续波形变换 (CWT) 用于放大故障信息和基于重建的累积贡献率的故障隔离标准.
主要成果:
- 拟议的Makima插值有效地处理大量持续丢失的传感器数据.
- 深度PCA和MSPCA框架增强了从多个角度提取故障特征,提高了检测准确度.
- 使用增强数据集和故障隔离标准实现了精确的故障隔离.
结论:
- 开发的数据归算和故障诊断框架显著提高了高速列车引系统初始故障检测和隔离的可靠性和准确性.
- 集成先进的数据处理技术解决了缺少传感器数据和复杂的操作环境所带来的实际挑战.
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