Cargando…
Deep transfer learning strategy for efficient domain generalisation in machine fault diagnosis
Automated fault diagnosis algorithms based on vibration sensor recordings play an important role in determining the state of health of the machines. Data-driven approaches demand a large amount of labelled data to build reliable models. The performance of such lab-trained models degrades when deploy...
Autores principales: | Asutkar, Supriya, Tallur, Siddharth |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10125977/ https://www.ncbi.nlm.nih.gov/pubmed/37095230 http://dx.doi.org/10.1038/s41598-023-33887-5 |
Ejemplares similares
-
Deep reinforcement learning-based pairwise DNA sequence alignment method compatible with embedded edge devices
por: Lall, Aryan, et al.
Publicado: (2023) -
Adversarial Deep Transfer Learning in Fault Diagnosis: Progress, Challenges, and Future Prospects
por: Guo, Yu, et al.
Publicado: (2023) -
Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning
por: Qu, Xiaofei, et al.
Publicado: (2023) -
A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention
por: Liu, Caiming, et al.
Publicado: (2022) -
Lite and Efficient Deep Learning Model for Bearing Fault Diagnosis Using the CWRU Dataset
por: Yoo, Yubin, et al.
Publicado: (2023)