Research on Motor Fault Identification Method Based on Vibration Current Multisource Data Fusion and Deep Learning-Reference-Cited by-同舟云学术

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Research on Motor Fault Identification Method Based on Vibration Current Multisource Data Fusion and Deep Learning

Published:2023-10-12 Issue: Volume:51 Page:1-5
ISSN:
Container-title:2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
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Author:

He Yiyi¹,Feng Kun²,Liu Baoxia³

Affiliation:

1. Beijing University of Chemical Technology,Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education,Beijing,China,100029

2. Beijing University of Chemical Technology,State Key Laboratory of High-end Compressor and System Technology,Beijing,China,100029

3. National pipe networknorth company pipeline compressor unit maintenance center, Beijing University of Chemical Technology,Beijing,China,100029

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10482420/10482375/10482377.pdf?arnumber=10482377

Reference7 articles.

1. Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

2. A Hybrid Monitoring Technique for Diagnosis of Mechanical Faults in Induction Motor

3. Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine

4. Fault diagnosis of rolling bearing based on wavelet transform and envelope spectrum correlation

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