基于P-L双重特征提取的PEMFC系统故障诊断方法
FAULT DIAGNOSIS METHOD OF PEMFC SYSTEM BASED ON P-L DUAL FEATURE EXTRACTION
贺飞 1张雪霞 1陈维荣1
作者信息
- 1. 西南交通大学电气工程学院,成都 611756
- 折叠
摘要
针对质子交换膜燃料电池系统故障诊断问题,提出基于P-L双重特征提取的故障诊断方法.使用P-L双重特征提取对预处理后的样本数据进行特征提取,通过冗余变量剔除与二次特征提取,最大程度保留分类特征并有效降低样本数据维度.利用二叉树多类支持向量机与极限学习机对二维故障特征向量进行分类实现故障诊断.通过实例验证,对比线性判别分析的特征提取效果,P-L双重特征提取可使相同分类器测试集诊断准确率提高21.19%,诊断准确率达99.27%,实现了PEMFC系统膜干、氢气供应故障的精准快速诊断.
Abstract
For the fault diagnosis of proton exchange membrane fuel cell(PEMFC)system,a fault diagnosis method based on P-L dual feature extraction was proposed.P-L dual feature extraction is used to extract features from the preprocessed sample data.Through redundant variable removal and secondary feature extraction,classification features are preserved to the maximum extent and the dimension of sample data is effectively reduced.Binary tree multi-class support vector machine and extreme learning machine are used to classify 2D fault feature vectors and realize fault diagnosis.Through the example verification,compared with the feature extraction effect of linear discriminant analysis,P-L dual feature extraction improves the diagnostic accuracy of the test set of the same classifier by 21.19%,and the diagnostic accuracy reaches 99.27%,realizing the accurate and rapid diagnosis of membrane dry and hydrogen supply faults in PEMFC system.
关键词
质子交换膜燃料电池/故障检测/数据挖掘/P-L双重特征提取/支持向量机/极限学习机Key words
proton exchange membrane fuel cell(PEMFC)/fault detection/data mining/P-L dual feature extraction/support vector machine(SVM)/extreme learning machine(ELM)引用本文复制引用
基金项目
四川省科技厅重点研发计划(22ZDYF3375)
西南交通大学基础研究培育支持计划(2682022ZTPY024)
成都国佳电气工程有限公司资助项目(NEEC-2022-B010)
出版年
2024
NETL
NSTL
维普
万方数据