Motor Bearings Fault Classification using CatBoost Classifier

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

Research output: Contribution to journalArticle (journal)peer-review


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.

Original languageEnglish
Pages (from-to)454-457
Number of pages4
JournalRenewable Energy and Power Quality Journal
Publication statusPublished - 30 Sept 2022


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


Dive into the research topics of 'Motor Bearings Fault Classification using CatBoost Classifier'. Together they form a unique fingerprint.

Cite this