A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

Fault diagnosis of rolling element bearings is an effective technology to ensure the steadiness of rotating machineries. Most of the existing fault diagnosis algorithms are supervised methods and generally require sufficient labeled data for training. However, the acquisition of labeled samples is o...

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Bibliographic Details
Main Authors: Xinan Chen, Zhipeng Wang, Zhe Zhang, Limin Jia, Yong Qin
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Sensors
Subjects:
rolling element bearing
semi-supervised learning
visibility graph
imbalanced data
Online Access:http://www.mdpi.com/1424-8220/18/7/2097

Internet

http://www.mdpi.com/1424-8220/18/7/2097

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