从时间序列建立复杂网络的有限可穿透可见度滑动图
Shilin Wang1,2, Peng Li1,2, Guangwu Chen1,2
1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.
Chaos (Woodbury, N.Y.)
|April 19, 2024
概括
一种新的网络建模方法,滑动窗有限可穿透可见度图 (SLPVG),准确地识别了轨道电路中的补偿电容故障. 这种方法将时间序列数据转换为网络,实现 99.1% 的故障诊断准确度.
科学领域:
- 网络科学 网络科学
- 时间序列分析时间序列分析.
- 铁路工程 铁路工程是指铁路工程.
背景情况:
- 时间序列数据通常含有噪声,并表现出复杂的动态.
- 传统的网络模型可能无法有效地捕捉时间序列的时间特征.
- 诊断铁路轨道电路中的故障,特别是补偿电容器,需要强大的分析方法.
研究的目的:
- 引入一种新的网络建模方法,滑动窗有限可穿透可见度图 (SLPVG),用于时间序列分析.
- 应用SLPVG模型来准确诊断无关节轨道电路中的补偿电容故障.
- 为了评估网络拓指标的有效性,如中间中心性,在故障检测.
主要方法:
- 开发了滑窗有限可穿透可见度图 (SLPVG) 算法,以将时间序列转换为网络结构.
- 分析了各种时间序列的度分布,以验证SLPVG捕获动态特征的能力.
- 补偿电容器的综合故障特征与网络拓指标,重点关注之间的中心性.
- 进行了实验,以测试SLPVG模型在识别补偿电容故障方面的性能.
主要成果:
- SLPVG模型有效地捕捉了具有低计算复杂度的时间序列的动态特征.
- 区间中心性被确定为准确反映补偿电容器故障状态的关键指标.
- 拟议的模型在识别无关节轨道电路中的补偿电容器故障时,达到99.1%的高准确率.
结论:
- SLPVG模型提供了一种简单,高效和准确的方法来分析长时间序列动态.
- 这种方法为诊断无关节轨道电路中的补偿电容器故障提供了一个新的视角.
- 该研究表明,这对推进时间序列分析和铁路故障检测研究具有重大实际意义.
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