波聚合:对时间序列的高度区分的波特征估计器
Ye Wang1, Zhentao Yu1, Cheng Chi1
1Naval Submarine Academy, Qingdao 266199, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
一种新方法 - - 波聚合 (HaAgEn) 准确地检测电力系统中的波特征. 这种方法可以改进信号分析,以提高大型船舶等应用中的安全性和效率.
科学领域:
- 信号处理 信号处理
- 电气工程 电气工程
- 数据分析 数据分析
背景情况:
- 波在电力系统中普遍存在,导致能源消耗增加以及对设备安全和性能的风险增加,特别是在大型船舶等关键应用中.
- 现有的时间频率分析方法用于波检测,其计算成本高,特征提取特异性有限.
- 需要先进的方法来准确检测和表征时间序列数据中的子元件.
研究的目的:
- 引入一种新的波特征估计方法,波聚合 (HaAgEn),旨在在时间序列数据中有效检测波.
- 解决传统方法的局限性,通过开发一个更高效的计算和特定的和特征提取技术.
- 验证HaAgEn在区分和声信号与背景噪声方面的有效性,并提高检测精度.
主要方法:
- 拟议的方法HaAgEn基于双光谱分析,利用双光谱矩阵内波信号的独特分布.
- 使用一种新的对角双向整合双光谱 (DBIB) 技术,从双光谱矩阵中提取和特征.
- 交叉用于计算不同频轴上的DBIB的集成结果 (Ix和Iy),形成HaAgEn度量.
主要成果:
- 与其他基于的方法相比,HaAgEn对子元件的灵敏度明显更高,有效地减少了特征冗余.
- 该方法成功地对背景噪声进行了歧视,提供了更具体的声特征提取.
- 海上试验数据分析显示,HaAgEn在检测轴速电磁场信号中的子元件方面达到96.8%的准确性,超过现有方法.
结论:
- HaAgEn提供了一种新且有效的技术方法,用于工业应用中的波检测,特别是用于时间序列数据分析.
- 该方法在检测准确性和效率上比传统技术提供了显著的改进.
- HaAgEn的增强灵敏度和特异性使其成为确保电力系统和相关设备的安全性和性能的宝贵工具.
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