A Hybrid Diagnosis for Gas Starvation Faults in Proton Exchange Membrane Fuel Cells

Sani, Mukhtar; Piffard, Maxime; Heiries, Vincent

doi:10.1109/cdc49753.2023.10384204

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2024

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Cited by 2 publications

References 18 publications

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Machine Learning-Based Fault Diagnosis for Reactants Starvation with Severity Levels in PEMFC Under Dynamic Load Profile

Sani,

Piffard,

Heiries

2024

2024 Prognostics and System Health Management Conference (PHM)

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Machine Learning-Based Fault Diagnosis for Reactants Starvation with Severity Levels in PEMFC Under Dynamic Load Profile

Sani,

Piffard,

Heiries

2024

2024 Prognostics and System Health Management Conference (PHM)

View full text Add to dashboard Cite

Application of Multiple Linear Regression and Artificial Neural Networks in Analyses and Predictions of the Thermoelectric Performance of Solid Oxide Fuel Cell Systems

Lai,

Zhang,

et al. 2024

Energies

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Solid oxide fuel cells (SOFCs) are an efficient, reliable and clean source of energy. Predictive modeling and analysis of their performance is becoming increasingly important, especially with the growing emphasis on sustainable development’s requirements. However, mathematical modeling is difficult due to the complexity of its internal structure. In this study, the system’s electricity generating performance and operational characteristics were analyzed using recent on-site monitoring data first. Then, based on Pearson’s correlation coefficient, some of the variables were selected to build two prediction models: an artificial neural network (ANN) model and a multiple linear regression (MLR) model. The models were evaluated on the basis of the normalized mean square error (NRMSE), which was 1.89% for the MLR model and 0.66% for the ANN model, with no overall bias. They were also compared with other existing models, and it was found that the two models used in this study have the advantage of high accuracy and low difficulty. Therefore, the models developed in this study can more accurately and effectively assess the SOFC system’s state and can better support work to improve the thermoelectric performance of SOFC systems.

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A Hybrid Diagnosis for Gas Starvation Faults in Proton Exchange Membrane Fuel Cells

Cited by 2 publications

References 18 publications

Machine Learning-Based Fault Diagnosis for Reactants Starvation with Severity Levels in PEMFC Under Dynamic Load Profile

Machine Learning-Based Fault Diagnosis for Reactants Starvation with Severity Levels in PEMFC Under Dynamic Load Profile

Application of Multiple Linear Regression and Artificial Neural Networks in Analyses and Predictions of the Thermoelectric Performance of Solid Oxide Fuel Cell Systems

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