A Novel Machine Learning-Based Approach for Induction Machine Fault Classifier Development—A Broken Rotor Bar Case Study

Mikko Tahkola (Corresponding Author), Áron Szücs, Jari Halme, Akhtar Zeb, Janne Keränen

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
40 Downloads (Pure)


Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and measured vibration acceleration data. We introduce an approach that combines sequential model-based optimization and the nested cross-validation procedure to provide a reliable estimation of the classifiers’ generalization performance. These methods have not been combined earlier in this context. Automation of selected parts of the modeling procedure is studied with the measured data. We compare the performance of logistic regression and CatBoost models using the fast Fourier-transformed signals or their extracted statistical features as the input data. We develop a technique to use domain knowledge to extract features from specific frequency ranges of the fast Fourier-transformed signals. While both approaches resulted in similar accuracy with simulated current and measured vibration acceleration data, the feature-based models were faster to develop and run. With measured vibration acceleration data, better accuracy was obtained with the raw fast Fourier-transformed signals. The results demonstrate that an accurate and fast broken rotor bar detection model can be developed with the presented approach.
Original languageEnglish
Article number3317
Number of pages23
Issue number9
Publication statusPublished - 2 May 2022
MoE publication typeA1 Journal article-refereed


  • broken rotor bar
  • condition monitoring
  • fault classification
  • feature extraction
  • induction machine
  • machine learning
  • predictive maintenance
  • supervised learning


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