CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things

Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution...

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Detalles Bibliográficos
Autores principales: Gui, Zhenwen, He, Shuaishuai, Lin, Yao, Nan, Xin, Yin, Xiaoyan, Wu, Chase Q.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459481/
https://www.ncbi.nlm.nih.gov/pubmed/37631576
http://dx.doi.org/10.3390/s23167040

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459481/
https://www.ncbi.nlm.nih.gov/pubmed/37631576
http://dx.doi.org/10.3390/s23167040

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