Development on Diagnosing Method of Metallic Micro-particles Buried into Fuel Cell Membrane Electrode Assemblies using Electromagnetic Field Excited Oscillation

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  • 電磁場誘起振動を用いた燃料電池膜電極接合体における微小金属異物検出システムの開発
  • - Automatic Diagnosing System based on Machine Learning
  • 機械学習に基づく自動検出システム

Abstract

<p>In the manufacturing process of Membrane Electrode Assemblies (MEA) of fuel cell, there remains a problem of metallic micro-particles buried into Gas Diffusion Layer (GDL). It leads to the performance drop-down of fuel cells. In this study, we propose a real-time diagnosing system introducing Machine Learning to EMA-LDS, which is composed of electro-magnetic impact generators and laser displacement sensors. EMA-LDS was experimentally applied to both samples of gas diffusion layer (GDL) with or without burying Fe micro-particle with a diameter of 100 μm. As an experimental result, the contaminated GDL was estimated to have a high probability of metallic micro-particles, although the normal GDL had lower probability. In conclusions, the proposed method can discriminate metallic micro-particles according to Machine learning. Therefore, EMA-LDS has an effective potential as a diagnosing system of metallic microparticle into MEA.</p>

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