A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals-Reference-Cited by-同舟云学术

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A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

Published:2022-11-07 Issue: Volume: Page:
ISSN:
Container-title:2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
language:
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Author:

Zim Abid Hasan¹,Ashraf Aeyan²,Iqbal Aquib²,Malik Asad³,Kuribayashi Minoru⁴

Affiliation:

1. Aligarh Muslim University,Department of Mechanical Engineering,Aligarh,India,202002

2. Aligarh Muslim University,Department of Computer Engineering,Aligarh,India,202002

3. Aligarh Muslim University,Department of Computer Science,Aligarh,India,202002

4. Graduate School of Natural Science and Technology, Okayama University,Okayama,Japan,700–8530

Funder

JSPS KAKENHI

JST SICORP

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9979726/9979808/09980013.pdf?arnumber=9980013

Reference38 articles.

1. Rolling bearing fault diagnosis using an optimization deep belief network

2. Rolling element bearing fault diagnosis using convolutional neural network and vibration image

3. A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

4. A physics-informed deep learning approach for bearing fault detection

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1. Smart manufacturing with transfer learning under limited data: Towards Data-Driven Intelligences;Materials Today Communications;2023-12

2. A Comprehensive Interturn Fault Severity Diagnosis Method for Permanent Magnet Synchronous Motors Based on Transformer Neural Networks;IEEE Transactions on Industrial Informatics;2023-11

3. Smart Manufacturing with Transfer Learning Under Limited Data: Towards Data-Driven Intelligences;2023

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