Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
Rolling bearings are the most fault-prone parts in rotating machinery. In order to find faults in time and reduce losses, this paper presents an intelligent diagnosis method for rolling bearings. At present, the deep residual network (RESNET) is the most widely used convolutional neural network (CNN...
Enregistré dans:
Auteurs principaux: | Xinyu Hao, Yuan Zheng, Li Lu, Hong Pan |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/3ac15ed7254c4b569b5b9452548dfccf |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review
par: Yuanyuan Yang, et autres
Publié: (2021) -
Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning
par: Chia-Ming Tsai, et autres
Publié: (2021) -
Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
par: Shih-Lin Lin
Publié: (2021) -
Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
par: Hong Tang, et autres
Publié: (2020) -
A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Compressed Sensing and Stacked Multi-Granularity Convolution Denoising Auto-Encoder
par: Chuang Liang, et autres
Publié: (2021)