Data-driven proton exchange membrane fuel cell degradation predication through deep learning method
• Deep learning method is used to predict fuel cell degradation. • G-LSTM cell based RNN is deployed for the prognostic. • Aging experimental tests with different fuel cells are conducted. • The G-LSTM can make predictions within the same framework. • The proposed prognostic model can be applied to...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ma, Rui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Risky business: Psychopathy, framing effects, and financial outcomes - Costello, Thomas H. ELSEVIER, 2018, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:231 ; year:2018 ; day:1 ; month:12 ; pages:102-115 ; extent:14 |
| Links: |
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| DOI / URN: |
10.1016/j.apenergy.2018.09.111 |
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ELV044652224 |
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| 520 | |a • Deep learning method is used to predict fuel cell degradation. • G-LSTM cell based RNN is deployed for the prognostic. • Aging experimental tests with different fuel cells are conducted. • The G-LSTM can make predictions within the same framework. • The proposed prognostic model can be applied to online diagnosis. | ||
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| 650 | 7 | |a Long short-term memory |2 Elsevier | |
| 650 | 7 | |a Fuel cell |2 Elsevier | |
| 650 | 7 | |a Prognostics |2 Elsevier | |
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| 700 | 1 | |a Breaz, Elena |4 oth | |
| 700 | 1 | |a Li, Zhongliang |4 oth | |
| 700 | 1 | |a Briois, Pascal |4 oth | |
| 700 | 1 | |a Gao, Fei |4 oth | |
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• Deep learning method is used to predict fuel cell degradation. • G-LSTM cell based RNN is deployed for the prognostic. • Aging experimental tests with different fuel cells are conducted. • The G-LSTM can make predictions within the same framework. • The proposed prognostic model can be applied to online diagnosis. |
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• Deep learning method is used to predict fuel cell degradation. • G-LSTM cell based RNN is deployed for the prognostic. • Aging experimental tests with different fuel cells are conducted. • The G-LSTM can make predictions within the same framework. • The proposed prognostic model can be applied to online diagnosis. |
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• Deep learning method is used to predict fuel cell degradation. • G-LSTM cell based RNN is deployed for the prognostic. • Aging experimental tests with different fuel cells are conducted. • The G-LSTM can make predictions within the same framework. • The proposed prognostic model can be applied to online diagnosis. |
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Data-driven proton exchange membrane fuel cell degradation predication through deep learning method |
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