A method for automotive fuel cell fault diagnosis based on PCA-APSO-SVM

Yiming Zhang; Changqing Du; Tongyu Pan

doi:10.1117/12.2679089

25 May 2023 A method for automotive fuel cell fault diagnosis based on PCA-APSO-SVM

Yiming Zhang, Changqing Du, Tongyu Pan

Author Affiliations +

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120Z (2023) https://doi.org/10.1117/12.2679089
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

For two types of common faults in automotive fuel cell reactors, namely flooding and drying, a PEMFC fault diagnosis approach combining support vector machine (SVM) and adaptive particle swarm optimization (APSO) is proposed. In this study, principal component analysis (PCA) is used to reduce the dimensionality of the original data samples, extract the two key variables characterizing the faults, establish the APSO-SVM diagnostic model, and compare and contrast it with RNN, CNN, and BP diagnostic methods to demonstrate the effectiveness and superiority of the algorithm. To compare and evaluate the diagnostic model's generalizability, several sample set sizes are used. The simulation outcomes demonstrate that the fault diagnosis method of optimized SVM using APSO has excellent performance in terms of diagnostic accuracy, model training speed, and generalization ability, providing a method reference for further research on automobile fuel cell fault detection.

Citation Download Citation

Yiming Zhang, Changqing Du, and Tongyu Pan "A method for automotive fuel cell fault diagnosis based on PCA-APSO-SVM", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120Z (25 May 2023); https://doi.org/10.1117/12.2679089

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