控制与信息技术Issue(2)：87-94,8.DOI:10.13889/j.issn.2096-5427.2026.02.009
基于多协同映射核Fisher判别分析的多维状态监测数据特征提取方法
Feature Extraction from Multi-Dimensional Condition Monitoring Data Based on Multiple Co-Mapping Kernel Fisher Discriminant Analysis
摘要
Abstract
Conventional kernel-based Fisher discriminant analysis(KFDA)is limited in adaptability of kernel functions for feature extraction from condition monitoring data.To address this,this paper proposes a feature extraction method based on multiple co-mapping kernel Fisher discriminant analysis(MCKFDA),which incorporates kernel function optimization into the conventional framework.Firstly,a data-dependent kernel function is constructed to adapt the kernel structure to various types of condition monitoring data collected;on this basis,a multiple co-mapping kernel structure is devised to accommodate diversity across data samples.Then,the maximun margin criterion(MMC)is employed to achieve the optimal combination weights of the multiple co-mapping kernel,while the Fisher criterion is used to find the optimal data-dependent kernel parameters.The proposed approach was assessed using a lithium-ion battery dataset.Experimental results show that,compared with traditional kernel discriminant analysis,the proposed MCKFDA method improves the mean square error by 15.4%for lithium-ion battery capacity estimation,representing a substantial enhancement in accuracy of performance evaluation.关键词
状态监测/特征提取/性能评估/核Fisher判别分析/多协同映射/锂离子电池Key words
condition monitoring/feature extraction/performance assessment/kernel Fisher discriminant analysis/multiple co-mapping/lithium-ion battery分类
信息技术与安全科学引用本文复制引用
吕宇,别必龙,徐勇..基于多协同映射核Fisher判别分析的多维状态监测数据特征提取方法[J].控制与信息技术,2026,(2):87-94,8.基金项目
国家重点研发计划项目(2022YFB4300602) （2022YFB4300602）
