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Induction motor fault classification via entropy and column correlation features of 2D represented vibration data
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Vocational School of Transportation, Department of Motor Vehicles and Transportation Technologies, Eskisehir Technical University, 26140 Odunpazari, Eskisehir, Turkey
Publication date: 2021-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(1):132–142
  • Induction motor bearing defects are classified due to the defect type as well as depth.
  • Normalised and partitioned 1D vibration signals are converted into 2D greyscale images.
  • 2D discrete wavelet transform is applied to greyscale images to create four sub-images.
  • Novel entropy and column correlation-based features are extracted from these sub-images.
  • Comparative classification success of the proposed feature extraction method is shown.
Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.
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