Generalized fault diagnostics of polymer electrolyte fuel cells using machine learning.

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

SUBMITTER: D'Silva G 

PROVIDER: S-EPMC12446379 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature