An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set
Abstract Accurately identifying bearing faults in aeroengines is crucial for maintaining their lifespan and cost. However, most current models are black-box models, such as deep learning models such as deep neural networks. The decision-making process of these models is more complex and lacks interp...
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| Главные авторы: | , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Nature Portfolio
2024-12-01
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| Серии: | Scientific Reports |
| Предметы: | |
| Online-ссылка: | https://doi.org/10.1038/s41598-024-82804-x |
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