Multi-domain Bearing Fault Diagnosis using Support Vector Machine-Reference-Cited by-同舟云学术

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Multi-domain Bearing Fault Diagnosis using Support Vector Machine

Published:2021-09-24 Issue: Volume: Page:
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Container-title:2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON)
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Author:

Mishra Rismaya Kumar,Choudhary Anurag,Mohanty Amiya Ranjan,Fatima Shahab

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9573501/9573519/09573613.pdf?arnumber=9573613

Cited by 15 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. Realistic Condition-Based Anomaly Detection of Multi-Faults in Rotating Machines;2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET);2023-08-09

2. Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing;Sensors;2023-06-15

3. Fault Diagnosis of Electric Two-Wheeler Under Pragmatic Operating Conditions Using Wavelet Synchrosqueezing Transform and CNN;IEEE Sensors Journal;2023-03-15

4. Passive Thermography Based Bearing Fault Diagnosis Using Transfer Learning With Varying Working Conditions;IEEE Sensors Journal;2023-03-01

5. Anomalous Sound Detection for Industrial Machines Using Acoustical Features Related to Timbral Metrics;IEEE Access;2023

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