D'Silva, Greg;
Mahmood, Eashaal;
Jervis, Rhodri;
Zhou, Shangwei;
(2025)
Generalized fault diagnostics of polymer electrolyte fuel cells using machine learning.
iScience
, 28
(9)
, Article 113350. 10.1016/j.isci.2025.113350.
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Abstract
Polymer electrolyte fuel cells (PEFCs) are promising for mobile and stationary applications, but short operational lifetimes and frequent faults limit their commercial viability. This work introduces a black-box diagnostic method using multifrequency Walsh function perturbation signals to detect water management and starvation faults. The approach improves signal-to-noise ratio and accuracy without harming the cell. Using voltage response as the diagnostic variable, dense neural networks (DNNs), 1D convolutional neural networks (1D-CNNs), and support vector machines (SVMs) were tested. All models accurately classified normal, drying, and starvation conditions in a single PEFC, with 1D-CNN and SVMs reaching 100% accuracy. However, model generalization to a different PEFC was poor. Including data from multiple PEFCs significantly improved performance, with the 1D-CNN showing superior generalization, even when trained on limited unseen data. This establishes the 1D-CNN as the most effective model for robust, scalable PEFC diagnostics across varied datasets.
| Type: | Article |
|---|---|
| Title: | Generalized fault diagnostics of polymer electrolyte fuel cells using machine learning |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.isci.2025.113350 |
| Publisher version: | https://doi.org/10.1016/j.isci.2025.113350 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10217227 |
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