Cargando…
Supervised Health Stage Prediction Using Convolutional Neural Networks for Bearing Wear
Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal f...
Autores principales: | Suh, Sungho, Jang, Joel, Won, Seungjae, Jha, Mayank Shekhar, Lee, Yong Oh |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602811/ https://www.ncbi.nlm.nih.gov/pubmed/33081097 http://dx.doi.org/10.3390/s20205846 |
Ejemplares similares
-
Prognosis of Bearing and Gear Wears Using Convolutional Neural Network with Hybrid Loss Function
por: Lo, Chang-Cheng, et al.
Publicado: (2020) -
Weakly-supervised convolutional neural networks for multimodal image registration
por: Hu, Yipeng, et al.
Publicado: (2018) -
Application of Convolutional Neural Network in Motor Bearing Fault Diagnosis
por: Zhou, Shuiqin, et al.
Publicado: (2022) -
Brain CT registration using hybrid supervised convolutional neural network
por: Yuan, Hongmei, et al.
Publicado: (2021) -
Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans
por: Usman, Muhammad, et al.
Publicado: (2022)