End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis

End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis

Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault...

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Bibliographic Details
Main Authors: Jianbo Zheng, Jian Liao, Zongbin Chen
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Sensors
Subjects:
fault diagnosis
rolling bearing
deep learning
LSTM
CNN
attention
Online Access:https://www.mdpi.com/1424-8220/22/17/6489

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