Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells
Overview & Synopsis
Health indicator of fuel cell under dynamic operating conditions is extracted. • Extracted health indicator has clear trend and can also indicate various faults. • Symbolic-based deep learning captures historical trend and retains it in prediction. • Hybrid approach provides wide prognostic horizon and credible lifetime estimation.
Indexed from repository: HAL Open Science
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hal.science)
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Document Information
| Document Title | Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells |
|---|---|
| Author / Organization | Chu Wang, Zhongliang Li, Rachid Outbib, Manfeng Dou, Dongdong Zhao |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2022 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-230033 |
| Original Source Link | Visit Source Page on hal.science » |