In this paper we present results from our recent work in which polymer electrolyte membrane fuel cell electrodes with intentionally introduced known defects were imaged and analyzed using a fuel cell scanner recently developed at the National Renewable Energy Laboratory. The defect types considered included particle debris, scuffs, scores, slits, and laser perforated pinholes. The debris defects were analyzed on samples from three different production stages, whereas the other defect types were introduced in a membrane tacked on a catalyst-coated diffusion media. We are showing that the fuel cell scanner can generate good quality, high resolution images of both baseline and defect-containing material. Based on the scanned images, an automatic, computer vision algorithm is developed that identifies presence and location of debris particles. The presented results clearly indicate that the in-line visible-light-diffuse-reflectance-based system can be successfully employed to monitor quality and to detect critical defects in fuel cell electrodes that are transported with high speed in a high volume manufacturing facility.

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