一种适应性参数化域映射方法及其在铁路车辆的车轮铁路合故障诊断中的应用
Zihang Xu1,2, Jianwei Yang1,2, Dechen Yao1,2
1Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
准确检测合的车轮 - 轨道故障,如波纹和平面痕,对于铁路车辆的安全至关重要. 本研究介绍了一种优化的时间频率分析方法,用于在变速下可靠的故障诊断.
科学领域:
- 铁路车辆动态和故障诊断.
- 先进的信号处理技术.
- 工程应用的优化算法.
背景情况:
- 城市化增加了铁路车辆的运行需求,导致了车轮轨道故障 (波纹,多角化,平面痕).
- 这些合故障会降低车轮-轨道接口的性能,危及驾驶安全.
- 准确检测这些故障对于提高运营安全至关重要.
研究的目的:
- 开发一种可靠的方法来检测在变速条件下合的车轮轨道故障.
- 探索铁路波纹,多角化和平面痕的合特性.
- 提高铁路车辆故障诊断的准确性和效率.
主要方法:
- 铁路车辆的动态建模以模拟故障特征.
- 拟议的自适应概率数据方法 (APDM) 时间频率分析使用Rényi.
- 鱼优化算法 (WOA) 用于优化APDM参数,显示比PSO和SSA更快的融合.
主要成果:
- 在变速下,APDM方法有效地定位和提取合故障特征.
- 与传统方法相比,优化的APDM表现出更高的能量度和抗噪能力.
- WOA优化显著减少了代数,并提高了Rényi的精度.
结论:
- 由WOA优化的拟议APDM方法,为诊断合车轮-轨道故障提供了高度有效和高效的解决方案.
- 该方法的准确性和耐噪力通过模拟和实验来验证.
- 这项研究对提高铁路车辆安全具有重要的工程应用价值.
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