化工学报2024，Vol.75Issue(3)：945-955,11.DOI:10.11949/0438-1157.20231247
基于改进TSO优化Xception的PEMFC故障诊断
PEMFC fault diagnosis based on improved TSO optimized Xception
摘要
Abstract
This paper proposes a fault diagnosis method for proton exchange membrane fuel cells(PEMFC)based on Xception network optimized by an improved transient search optimization(TSO)algorithm.First,the fault data are normalized and dimensionally reduced by linear discriminant analysis,which reduces the computational complexity while preserving the main features.Secondly,the TSO algorithm is enhanced by introducing Tent chaotic mapping and reverse learning strategy,which improves its global search ability.The hyperparameters of the Xception neural network are optimized by the TSO algorithm in the training phase.Finally,the fully trained Xception network is used to classify and identify PEMFC faults,and compared with the classic classification model.On the experimental water management fault data and the simulated multi-class fault data,the Xception network achieves the highest classification accuracy,which is 100%and 98.08%,respectively.This indicates that the Xception network has a strong ability to extract data features and the proposed method can serve as a general diagnosis method for PEMFC faults.关键词
质子交换膜燃料电池/故障诊断/Tent混沌映射/反向学习/瞬态搜索优化/Xception神经网络Key words
proton exchange membrane fuel cell/fault diagnosis/Tent chaotic mapping/reverse learning/transient search optimization/Xception neural network分类
化学化工引用本文复制引用
张领先,刘斌,邓琳,任宇航..基于改进TSO优化Xception的PEMFC故障诊断[J].化工学报,2024,75(3):945-955,11.基金项目
国家重点研发计划项目(2021YFB2400700) （2021YFB2400700）
