Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning-Reference-Cited by-同舟云学术

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Diagnosis of Localized Bearing Defects using Texture Analysis of Vibration Envelope Signals and Machine Learning

Published:2023-03-03 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Robotics and Automation in Industry (ICRAI)
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Author:

Nguyen Cong Dai¹,Nasir Fazal E²,Khan Shahbaz³,Khan Sheraz Ali⁴,Kim Jong-Myon⁵

Affiliation:

1. Le Quy Don Technical University,Faculty of Radio-Electronic Engineering,Hanoi,Vietnam

2. Advanced Robotics and Automation Lab, National Center for Robotics and Automation,Peshawar,Pakistan

3. Institute of Manufacturing, University of Engineering and Applied Sciences,Swat,Pakistan

4. University of Engineering and Technology,Department of Mechatronics Engineering,Peshawar,Pakistan

5. University of Ulsan,Department of IT Convergence,Ulsan,South Korea

Funder

University of Engineering and Technology Peshawar, Pakistan

Ministry of SMEs and Startups(MSS, Korea)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10089530/10089531/10089574.pdf?arnumber=10089574

Reference14 articles.

1. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

2. Electron spectroscopy studies on magneto-optical media and plastic substrate interface;yorozu;IEEE Transl J Magn Japan,1987

3. Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

4. Improved Fault Diagnosis of Ball Bearings Based on the Global Spectrum of Vibration Signals

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