Optimized Machine Learning for Induction Motor Fault Diagnosis Using Vibration and Frequency-Domain Features
Received: 28 June 2025 | Revised: 23 August 2025 | Accepted: 6 September 2025 | Online: 5 October 2025
Corresponding author: Worawat Sa-Ngiamvibool
Abstract
This study uses vibration signal analysis to assess and contrast several machine learning models for identifying defects in induction motors. A comprehensive dataset obtained from TDMS-format sensor recordings included seven inter-turn short circuit fault conditions and one normal state. Three experimental settings were investigated: (i) multiclass classification with basic time-domain features, (ii) binary classification using enhanced time and Fast Fourier Transform (FFT) frequency-domain features, and (iii) optimal binary classification using hyperparameter tuning and advanced boosting models. KNN, Random Forest (RF), and ensemble models (XGBoost, LightGBM, CatBoost) were trained and evaluated using accuracy, MSE, RMSE, and R2. The results reveal that while raw time-domain features performed poorly in multiclass tasks (accuracy ~20%), significant gains were obtained utilizing FFT features and binary classification (accuracy up to 80%). Using hyperparameter tuning and gradient boosting techniques, additional enhancements drove the accuracy to 87%, with CatBoost and LightGBM excelling among others. These results highlight the importance of frequency-domain characteristics and model optimization in increasing fault detection dependability. In conclusion, this study helps to encourage the inclusion of intelligent monitoring systems into predictive maintenance pipelines, therefore prolonging the lifetime of industrial equipment and reducing operational downtime.
Keywords:
predictive maintenance, vibration signal processing, frequency-domain analysis, ensemble learning algorithms, feature engineeringDownloads
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Copyright (c) 2025 Somdavee Bhosinak, Sitthisak Audomsi, Niwat Angkawisittpan, Chonlatee Photong, Worawat Sa-Ngiamvibool
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