Deep Learning based Time-Frequency Image Enhancement Method for Machinery Health Monitoring-Reference-Cited by-同舟云学术

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Deep Learning based Time-Frequency Image Enhancement Method for Machinery Health Monitoring

Published:2023-06-28 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)
language:
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Author:

Choudhury Madhurjya D.¹,Blincoe Kelly²,Dhupia Jaspreet S.³

Affiliation:

1. Victoria University of Wellington,School of Engineering and Computer Science,Wellington,New Zealand,6012

2. University of Auckland,Department of Electrical, Computer, and Software Engineering,Auckland,New Zealand,1010

3. University of Auckland,Department of Mechanical Engineering and Mechatronics Engineering,Auckland,New Zealand,1010

Funder

University of Auckland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10196083/10196094/10196181.pdf?arnumber=10196181

Reference18 articles.

1. A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults

2. Deep digital twins for detection, diagnostics and prognostics

3. Vibration-based Condition Monitoring

4. A novel tacholess order analysis method for bearings operating under time-varying speed conditions

5. FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Deep Learning Based Fault Diagnosis Method Combining Domain Knowledge and Transfer Learning;2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP);2023-11-21

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