Bearing Fault Diagnosis Method Based on Attention Mechanism and Multi-Channel Feature Fusion-Reference-Cited by-同舟云学术

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Bearing Fault Diagnosis Method Based on Attention Mechanism and Multi-Channel Feature Fusion

Published:2024 Issue: Volume:12 Page:45011-45025
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Gao Hongfeng¹,Ma Jie²,Zhang Zhonghang³^ORCID,Cai Chaozhi²^ORCID

Affiliation:

1. Handan Branch of Hebei Special Equipment Supervision and Inspection Institute, Handan, China

2. School of Mechanical and Equipment Engineering, Hebei University of Engineering, Hebei, Handan, China

3. MCC Baosteel Technology Service Company Ltd., Shanghai, China

Funder

Market Supervision and Administration Bureau Research Plan Project of Hebei Province

Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10478912.pdf?arnumber=10478912

Reference31 articles.

1. A summary of fault modelling and predictive health monitoring of rolling element bearings

2. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings

3. Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks

4. Application of Time-Frequency Analysis in Rotating Machinery Fault Diagnosis

5. Application of Infrared Spectroscopy for Discrimination Between Electrical and Thermal Faults in Transformer Oil

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1. A Fault Prediction Method for CNC Machine Tools Based on SE-ResNet-Transformer;Machines;2024-06-18

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