Towards Deep Computer Vision for In-Line Defect Detection in Polymer Electrolyte Membrane Fuel Cell Materials

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5 Scopus Citations

Abstract

Polymer Electrolyte Membrane (PEM) fuel cells are a promising source of alternative energy. However, their production is limited by a lack of well-established methods for quality control of their constituent materials like the membrane-electrode assembly during roll-to-roll manufacturing. One potential solution is the implementation of deep learning methods to detect unwanted defects through their detection in scanned images. We explore the detection of defects like scratches, pinholes, and scuffs in a sample dataset of PEM optical images using two deep learning algorithms: Patch Distribution Modeling (PaDiM) for unsupervised anomaly detection and Faster-RCNN for supervised object detection. Both methods achieve scores on performance metrics (ROC-AUC and PRO-AUC for PaDiM and AP for Faster-RCNN) that are comparable to their scores on benchmark datasets. These methods also have the potential to detect a wider range of defects compared to IR thermography and previous optical inspection methods. Overall, deep learning shows promise at detecting relevant defects of interest and has the potential to achieve real-time defect detection.

Original languageAmerican English
Pages (from-to)18978-18995
Number of pages18
JournalInternational Journal of Hydrogen Energy
Volume48
Issue number50
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Hydrogen Energy Publications LLC

NREL Publication Number

  • NREL/JA-2C00-85189

Keywords

  • Anomaly detection
  • Defect detection
  • Electrode
  • Object detection
  • PEM
  • Pinhole

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