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Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. M...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145222/ https://www.ncbi.nlm.nih.gov/pubmed/35632345 http://dx.doi.org/10.3390/s22103936 |