Kökver Y., Koçkar N., Selçuk A. Y., Ekinci F., Orman A., Güzel M. S.
COMPUTERS AND INFORMATICS, cilt.2, sa.Advanced Online Publication, ss.36-57, 2026 (Hakemli Dergi)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
2
Sayı:
Advanced Online Publication
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Basım Tarihi:
2026
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Doi Numarası:
10.62189/ci.1974430
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Dergi Adı:
COMPUTERS AND INFORMATICS
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Derginin Tarandığı İndeksler:
Applied Science & Technology Source, Central & Eastern European Academic Source (CEEAS), Directory of Open Access Journals
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Sayfa Sayıları:
ss.36-57
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Ankara Üniversitesi Adresli:
Evet
Özet
Electric motors are critical components of modern industrial and transportation systems, making accurate fault diagnosis and predictive maintenance essential for operational reliability. This study proposes a unified machine learning framework for motor fault classification, temperature prediction, and remaining useful life (RUL) estimation using three publicly available electric motor datasets. The proposed pipeline integrates sliding-window segmentation, multi-domain feature extraction, consensus-based feature selection, and physically motivated descriptors derived from electrical and thermal measurements. Five machine learning algorithms, namely Random Forest, Support Vector Machine (SVM), XGBoost, LightGBM, and Long Short-Term Memory (LSTM), were systematically evaluated. For fault diagnosis on the Ottawa UOEMD-VAFCVS dataset, evaluated with a file-level train–test split in which all segments of a recording are kept in the same partition, LightGBM achieved the highest performance on the held-out recordings (Accuracy = 1.0000, F1-score = 1.0000, ROC-AUC = 1.0000). Sliding-window segmentation increased the number of trainable observations from 128 to 2,432 and improved the macro F1-score from 0.569 to above 0.994 across all models. For thermal prediction on the PMSM dataset, LightGBM obtained R² values of 0.993, 0.991, 0.989, and 0.987 for rotor, stator winding, stator tooth, and stator yoke temperatures, respectively, with mean absolute errors below 1.4°C. In an exploratory experiment on the synthetically generated EVIoT dataset, which contains no genuine run-to-failure trajectories, XGBoost and LightGBM reached R² = 0.933 on a 30-step smoothed RUL target; this value largely reflects the smoothing of the target and is reported only as a demonstration of the pipeline, not as validated prognostic capability. A Friedman test over N repeated runs indicated significant differences among the five classifiers (χ² = 11.556, p = 0.021, Kendall’s W = W). Within the evaluated configurations, feature-engineered gradient-boosting models matched or exceeded the LSTM baseline at low computational cost; a systematic comparison with end-to-end deep architecture is left for future work.