Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks

Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks

Traditional intelligent fault diagnosis works well when the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. However, in many real-world applications, the working conditions can vary between training and testing time. In this pape...

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Bibliographic Details
Main Authors: Bo Zhang, Wei Li, Xiao-Li Li, See-Kiong Ng
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Convolutional neural networks
domain adaptation
deep learning
intelligent fault diagnosis
transfer learning
Online Access:https://ieeexplore.ieee.org/document/8513748/

Internet

https://ieeexplore.ieee.org/document/8513748/

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