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Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions
The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal ac...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070631/ https://www.ncbi.nlm.nih.gov/pubmed/32102405 http://dx.doi.org/10.3390/s20041233 |