PEMFC fault diagnosis based on improved TSO optimized Xception
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.
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