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一个集体学习模型,用于评估的老化,考虑多功能组综合分析

Kun Zhang¹, Chuyan Zhang¹, Zhenan Zhou¹

¹School of Artificial Intelligence, China University of Geosciences, Beijing 100190, China.

|November 27, 2025

概括

这项研究引入了一种使用富里埃变换红外光谱 (FTIR) 和集体学习的新方法,以定量评估复合材料绝缘体中的老化. 开发的框架为电力系统的基于条件的维护提供了一个可靠的工具.

科学领域:

电气工程电气工程
材料科学材料科学材料科学
数据科学数据科学数据科学

背景情况:

复合材料绝缘体对于高压输电线路至关重要.
由于缺乏定量评估方法,中的材料老化阻碍了基于条件的维护.

研究的目的:

开发一种新的定量框架,用于评估复合绝缘体中的降解.
解决电气绝缘系统目前基于条件的维护策略的局限性.

主要方法:

福利埃变换红外光谱 (FTIR) 的集成用于定量光谱分析.
应用面向测量的集体学习模型 (KNN,SVM,RF,GBDT) 与动态重量分配.
利用信息理论特征选择和数据增强来提高模型性能和概括性.

主要成果:

在老化评估中获得了96.17%的准确性.
对噪声和异常有很强的模型稳定性,通过SHAP分析证实了超抗性.
建立了一个可扩展和可靠的定量衰老评估框架.

结论:

拟议的FTIR光谱学和集体学习框架为评估老化提供了一种可靠的方法.
这有助于为电气绝缘系统开发智能,数据驱动的诊断工具.
允许对电力基础设施进行改进的基于条件的维护策略.

关键词:

美国的FTIR是FTIR.评估衰老的评估.复合绝缘体是一种复合绝缘体.机器学习是机器学习.制的是一种.

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