Motor Bearings Fault Classification using CatBoost Classifier

Authors

  • Muhammad Irfan Author
  • Alwadie. A Author
  • Muhammad Awais Author
  • Saifur Rahman Author
  • Abdulkarem Hussein Mohammed Al Mawgani Author
  • Nordin Saad Author
  • Muhammad Aman Sheikh Author

DOI:

https://doi.org/10.24084/repqj20.339

Keywords:

Condition Monitoring, Time Domain Features, Fault Classification, CatBoost Classifier

Abstract

Induction motors are used in all industries and are the major element of energy consumption. Faults in motor degrade the motor efficiency and result in more energy consumption. Bearing faults are reported to be the major reason for the motor breakdown and a lot of papers have been reported to focus on bearing fault diagnostics. However, low classification accuracy is the main hurdle in adopting the available fault classification algorithms. This paper has presented a novel classification algorithm using the Catboost classifier and time domain features. The developed algorithm was tested on the laboratory test setup. The fault classification accuracy of 100 % was achieved through the proposed method.

Author Biographies

  • Muhammad Irfan

    Electrical Engineering Department, College of Engineering, Najran University, 
    Najran 61441, Saudi Arabia 

  • Alwadie. A

    Electrical Engineering Department, College of Engineering, Najran University, 
    Najran 61441, Saudi Arabia

  • Muhammad Awais

    Department of Computer Science, Edge Hill University, St Helens Rd., Ormskirk L39 4QP, UK

  • Saifur Rahman

    Electrical Engineering Department, College of Engineering, Najran University, 
    Najran 61441, Saudi Arabia 

  • Abdulkarem Hussein Mohammed Al Mawgani

    Electrical Engineering Department, College of Engineering, Najran University, 
    Najran 61441, Saudi Arabia 

  • Nordin Saad

    Faculty of Computing and Engineering, Quest International University, Malaysia.

  • Muhammad Aman Sheikh

    School of Engineering and Technology, Sunway University Malaysia 

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Published

2024-01-03

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Section

Articles