書誌事項
- タイトル別名
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- Development on Diagnosing Method of Metallic Micro-particles Buried into Fuel Cell Membrane Electrode Assemblies using Electromagnetic Field Excited Oscillation
- - Automatic Diagnosing System based on Machine Learning
- 機械学習に基づく自動検出システム
抄録
<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>
収録刊行物
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- 年次大会
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年次大会 2023 (0), S142p-02-, 2023
一般社団法人 日本機械学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390581070827335168
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- ISSN
- 24242667
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可