Spatiotemporal denoising wavelet network for infrared thermography-based machine prognostics integrating ensemble uncertainty
• A novel image stream-based model is proposed for prognostics integrating uncertainty. • A 4D wavelet convolutional layer is designed to extract interpretable features. • An image stream denoiser is developed by capturing spatiotemporal correlation. • An ensemble of deep networks is implemented for...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Yimin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Species loss from land use of oil palm plantations in Thailand - Jaroenkietkajorn, Ukrit ELSEVIER, 2021, mssp, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:173 ; year:2022 ; day:1 ; month:07 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ymssp.2022.109014 |
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ELV057464634 |
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| 520 | |a • A novel image stream-based model is proposed for prognostics integrating uncertainty. • A 4D wavelet convolutional layer is designed to extract interpretable features. • An image stream denoiser is developed by capturing spatiotemporal correlation. • An ensemble of deep networks is implemented for the uncertainty quantification. • Adaptive Student-t mixture distributions are constructed to fit the RUL values. | ||
| 650 | 7 | |a Infrared thermography |2 Elsevier | |
| 650 | 7 | |a Remaining useful life prediction |2 Elsevier | |
| 650 | 7 | |a Feature denoising |2 Elsevier | |
| 650 | 7 | |a 4D wavelet convolution layer |2 Elsevier | |
| 650 | 7 | |a Predictive uncertainty |2 Elsevier | |
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| 700 | 1 | |a Wang, Dong |4 oth | |
| 700 | 1 | |a Fang, Xiaolei |4 oth | |
| 700 | 1 | |a Xi, Lifeng |4 oth | |
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Spatiotemporal denoising wavelet network for infrared thermography-based machine prognostics integrating ensemble uncertainty |
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• A novel image stream-based model is proposed for prognostics integrating uncertainty. • A 4D wavelet convolutional layer is designed to extract interpretable features. • An image stream denoiser is developed by capturing spatiotemporal correlation. • An ensemble of deep networks is implemented for the uncertainty quantification. • Adaptive Student-t mixture distributions are constructed to fit the RUL values. |
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• A novel image stream-based model is proposed for prognostics integrating uncertainty. • A 4D wavelet convolutional layer is designed to extract interpretable features. • An image stream denoiser is developed by capturing spatiotemporal correlation. • An ensemble of deep networks is implemented for the uncertainty quantification. • Adaptive Student-t mixture distributions are constructed to fit the RUL values. |
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• A novel image stream-based model is proposed for prognostics integrating uncertainty. • A 4D wavelet convolutional layer is designed to extract interpretable features. • An image stream denoiser is developed by capturing spatiotemporal correlation. • An ensemble of deep networks is implemented for the uncertainty quantification. • Adaptive Student-t mixture distributions are constructed to fit the RUL values. |
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