A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation

Yunus Kökver; Neşet Koçkar; Ahmet Yasin Selçuk; Fatih Ekinci; Abdullah Orman; Mehmet Serdar Güzel

doi:10.62189/ci.1974430

A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation

Abstract

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.

Keywords

References

[1] Tiwari D, Miscandlon J, Tiwari A, Jewell GW. A review of circular economy research for electric motors and the role of Industry 4.0 technologies. Sustainability. 2021;13(17):9668. doi: https://doi.org/10.3390/su13179668
[2] Cai W, Wu X, Zhou M, Liang Y, Wang Y. Review and development of electric motor systems and electric powertrains for new energy vehicles. Automotive Innovation. 2021;4(1):3-22. doi: https://doi.org/10.1007/s42154-021-00139-z
[3] Ghemari Z, Belkhiri S. Improving the maintenance of electric motors: enhancing reliability and reducing downtime. Safety and Reliability. 2025:1-30. doi: https://doi.org/10.1080/09617353.2025.2594229
[4] Wróblewski A, Krot P, Zimroz R, Mayer T, Peltola J. Review of linear electric motor hammers—an energy-saving and eco-friendly solution in industry. Energies. 2023;16(2):959. doi: https://doi.org/10.3390/en16020959
[5] Sheikh MA, Bakhsh ST, Irfan M, Nor NBM, Nowakowski G. A review to diagnose faults related to three-phase industrial induction motors. Journal of Failure Analysis and Prevention. 2022;22(4):1546-1557. doi: https://doi.org/10.1007/s11668-022-01445-2
[6] Lokhande R, Mishra SK, Ronanki D, Shakya P, Edachery V, Koottaparambil L. Review on tribological and vibration aspects in mechanical bearings of electric vehicles: effect of bearing current, shaft voltage, and electric discharge material spalling current. Lubricants. 2025;13(8):349. doi: https://doi.org/10.3390/lubricants13080349
[7] Garcia-Calva T, Morinigo-Sotelo D, Fernandez-Cavero V, Romero-Troncoso R. Early detection of faults in induction motors—a review. Energies. 2022;15(21):7855. doi: https://doi.org/10.3390/en15217855
[8] Habyarimana M, Adebiyi AA. A review of artificial intelligence applications in predicting faults in electrical machines. Energies. 2025;18(7):1616. doi: https://doi.org/10.3390/en18071616

[9] Akbar S, Vaimann T, Asad B, Kallaste A, Sardar MU, Kudelina K. State-of-the-art techniques for fault diagnosis in electrical machines: advancements and future directions. Energies. 2023;16(17):6345. doi: https://doi.org/10.3390/en16176345
[10] Yousuf M, Alsuwian T, Amin AA, Fareed S, Hamza M. IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance. Measurement and Control. 2024;57(8):1146-1160. doi: https://doi.org/10.1177/0020294024123147
[11] Kumar RR, Andriollo M, Cirrincione G, Cirrincione M, Tortella A. A comprehensive review of conventional and intelligence-based approaches for the fault diagnosis and condition monitoring of induction motors. Energies. 2022;15(23):8938. doi: https://doi.org/10.3390/en15238938
[12] Shah R, Mittal V, Lotwin M. Recent advances in vibration analysis for predictive maintenance of modern automotive powertrains. Vibration. 2025;8(4):68. doi: https://doi.org/10.3390/vibration8040068
[13] Hamani K, Kuchar M, Kubatko M, Kirschner S. Advancements in induction motor fault diagnosis and condition monitoring: a comprehensive review. Sensors. 2025;25(19):5942. doi: https://doi.org/10.3390/s25195942
[14] Zong Z, Guan Y. AI-driven intelligent data analytics and predictive analysis in Industry 4.0: transforming knowledge, innovation, and efficiency. Journal of the Knowledge Economy. 2025;16(1):864-903. doi: https://doi.org/10.1007/s13132-024-02001-z
[15] Yuan S-M, Hong Z-W, Cheng W-K. Artificial intelligence and deep learning in sensors and applications. Sensors. 2024;24(10):3258. doi: https://doi.org/10.3390/s24103258
[16] Villegas-Ch W, Gaibor-Naranjo W, Sanchez-Viteri S. Application of deep learning techniques for the optimization of industrial processes through the fusion of sensory data. International Journal of Computational Intelligence Systems. 2024;17(1):187. doi: https://doi.org/10.1007/s44196-024-00596-4
[17] Wahid A, Breslin JG, Intizar MA. Prediction of machine failure in Industry 4.0: a hybrid CNN-LSTM framework. Applied Sciences. 2022;12(9):4221. doi: https://doi.org/10.3390/app12094221
[18] Vignesh RG, Fancy C. Predictive intelligence: a hybrid CNN-LSTM architecture with explainable AI for industrial machine failure prediction. In: 2026 4th International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA); 2026. p. 1-7. Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/AIMLA67915.2026.11522343
[19] Kirchgässner W, Wallscheid O, Böcker J. Estimating electric motor temperatures with deep residual machine learning. Institute of Electrical and Electronics Engineers Transactions on Power Electronics. 2021;36(7):7480-7488. doi: https://doi.org/10.1109/TPEL.2020.3045596
[20] Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13–17; San Francisco, CA, USA. New York (NY): Association for Computing Machinery; 2016. p. 785-794. doi: https://doi.org/10.1145/2939672.2939785
[21] Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems. 2017;30. Available from: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html
[22] Lei Y, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing. 2020;138:106587.
[23] Sehri M, Dumond P. University of Ottawa electric motor dataset – vibration and acoustic faults under constant and variable speed conditions (UOEMD-VAFCVS) [dataset]. Mendeley Data. 2026;2. doi: https://doi.org/10.17632/msxs4vj48g.2
[24] EVIoT-PredictiveMaint Dataset [dataset on the Internet]. Kaggle; 2026 [cited 2026 Apr 10]. Available from: https://www.kaggle.com/datasets/datasetengineer/eviot-predictivemaint-dataset/data
[25] Bousdekis A, Lepenioti K, Apostolou D, Mentzas G. A review of data-driven decision-making methods for Industry 4.0 maintenance applications. Electronics. 2021;10(7):828. doi: https://doi.org/10.3390/electronics10070828
[26] Garcia J, Rios-Colque L, Peña A, Rojas L. Condition monitoring and predictive maintenance in industrial equipment: an NLP-assisted review of signal processing, hybrid models, and implementation challenges. Applied Sciences. 2025;15(10):5465. doi: https://doi.org/10.3390/app15105465
[27] Haque BT, Rahman MA. Data preparation and feature engineering in high-dimensional predictive modeling. Journal of Sustainable Development and Policy. 2024;3(04):205-244. doi: https://doi.org/10.63125/5txtr530
[28] Miah MR, Islam MM. Data preprocessing and feature engineering strategies for large-scale predictive modeling applications. Review of Applied Science and Technology. 2024;3(01):263-302. doi: https://doi.org/10.63125/tqqqed47
[29] Umar M, Siddique MF, Kim J, Kim JM. A distribution-level statistical framework for reliable pipeline leak detection using multi-domain signal analysis. Scientific Reports. 2026. doi: https://doi.org/10.1038/s41598-026-56335-6
[30] Zimmermann M, Dostert K. Analysis and modeling of impulsive noise in broad-band powerline communications. Institute of Electrical and Electronics Engineers Transactions on Electromagnetic Compatibility. 2002;44(1):249-258. doi: https://doi.org/10.1109/15.990732
[31] Wang Y, Jiang H, Tong B, Song S. Rolling bearing fault diagnosis via Meta-BOHB optimized CNN–Transformer model and time-frequency domain analysis. Sensors. 2025;25(22):6920. doi: https://doi.org/10.3390/s25226920
[32] Hu B, Zhu J, Liang S, Ting L. Multi-domain representation learning for bearing fault diagnosis with phase and transient preservation. Applied Sciences. 2026;16(4):1846. doi: https://doi.org/10.3390/app16041846
[33] Yang Z. Systematic analysis on power spectral density estimation and its practical application. In: American Institute of Physics Conference Proceedings. Vol. 3435, No. 1; 2026. p. 020010. American Institute of Physics Publishing LLC. doi: https://doi.org/10.1063/5.0331080
[34] Duff EP, Johnston LA, Xiong J, Fox PT, Mareels I, Egan GF. The power of spectral density analysis for mapping endogenous BOLD signal fluctuations. Human Brain Mapping. 2008;29(7):778-790. doi: https://doi.org/10.1002/hbm.20601
[35] Nyangon J. Engineering hybrid physics-informed neural networks for next-generation electricity systems: a state-of-the-art review. arXiv preprint arXiv:2605.21903. 2026. doi: https://doi.org/10.48550/arXiv.2605.21903
[36] Du Z, Lu R. Physics-informed neural networks for advanced thermal management in electronics and battery systems: a review of recent developments and future prospects. Batteries. 2025;11(6):204. doi: https://doi.org/10.3390/batteries11060204
[37] Svetnik V, Wang T, Tong C, Liaw A, Sheridan RP, Song Q. Boosting: an ensemble learning tool for compound classification and QSAR modeling. Journal of Chemical Information and Modeling. 2005;45(3):786-799. doi: https://doi.org/10.1021/ci0500379
[38] Abimannan S, El-Alfy ESM, Chang YS, Hussain S, Shukla S, Satheesh D. Ensemble multifeatured deep learning models and applications: a survey. Institute of Electrical and Electronics Engineers Access. 2023;11:107194-107217. doi: https://doi.org/10.1109/ACCESS.2023.3320042
[39] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research. 2011;12:2825-2830.
[40] Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods. 2020;17(3):261-272. doi: https://doi.org/10.1038/s41592-019-0686-2
[41] Huang J, Tong J. Rolling bearing acoustic-vibration fusion fault diagnosis based on heterogeneous modal perception and knowledge distillation. Electronics. 2026;15(8):1631. doi: https://doi.org/10.3390/electronics15081631
[42] Garg S, et al. An overlapping sliding window and combined features based emotion recognition system for EEG signals. Applied Computing and Informatics. 2025;21(1-2):114-130. doi: https://doi.org/10.1108/ACI-05-2021-0130
[43] Sehri M, et al. Temporal domain vibration fault diagnosis of drone blades via selective embedding. Machines. 2026;14(2):241. doi: https://doi.org/10.3390/machines14020241
[44] Zeng M, et al. Research and application of a model selection forecasting system for wind speed and theoretical power generation. Future Internet. 2025;18(1):7. doi: https://doi.org/10.3390/fi18010007
[45] Xia Y, et al. Simulation-guided interpretable fault diagnosis of hydraulic directional control valves under limited fault data conditions. Sensors. 2026;26(7):2052. doi: https://doi.org/10.3390/s26072052
[46] Yadav OP, Pahuja GL. Bearing health assessment using time domain analysis of vibration signal. International Journal of Image, Graphics and Signal Processing. 2020;11(3):27. doi: https://doi.org/10.5815/ijigsp.2020.03.04
[47] Dua X, et al. Research on Bode data processing method based on Python. In: 2024 Institute of Electrical and Electronics Engineers 2nd International Conference on Control, Electronics and Computer Technology (ICCECT); 2024. Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/ICCECT60629.2024
[48] Giri MK, Majumder S. On eigenvalue-based cooperative spectrum sensing using feature extraction and maximum entropy fuzzy clustering. Journal of Ambient Intelligence and Humanized Computing. 2023;14(8):10053-10067. doi: https://doi.org/10.1007/s12652-021-03670-3
[49] Ameera A, Saidatul A, Ibrahim Z. Analysis of EEG spectrum bands using power spectral density for pleasure and displeasure state. Institute of Physics Conference Series: Materials Science and Engineering. 2019;557(1):012030. doi: https://doi.org/10.1088/1757-899X/557/1/012030
[50] Deng Y, et al. Physics-informed learning in artificial electromagnetic materials. Applied Physics Reviews. 2025;12(1). doi: https://doi.org/10.1063/5.0232675
[51] Yu W, et al. Comparative analysis of AC copper loss with round copper wire and flat copper wire of high-speed stator-PM flux-switching machine. Institute of Electrical and Electronics Engineers Transactions on Industry Applications. 2022;58(6):7131-7142. doi: https://doi.org/10.1109/TIA.2022.3194042
[52] Zhou A, et al. Rotor temperature safety prediction method of PMSM for electric vehicle on real-time energy equivalence. Mathematical Problems in Engineering. 2020;2020(1):3213052. doi: https://doi.org/10.1155/2020/3213052
[53] Salsabila A, Sari AP, Junaidi A. Predicting CO levels using LSTM and rolling-features. Journal of Artificial Intelligence and Engineering Applications. 2025;4(3):2178-2183. doi: https://doi.org/10.59934/jaiea.v4i3.1126
[54] Hou Y, et al. Evidential domain adaptation for remaining useful life prediction with incomplete degradation. Institute of Electrical and Electronics Engineers Transactions on Instrumentation and Measurement. 2025. doi: https://doi.org/10.1109/TIM.2025.3551977
[55] Robindro K, et al. Hybrid distributed feature selection using particle swarm optimization-mutual information. Data Science and Management. 2024;7(1):64-73. doi: https://doi.org/10.1016/j.dsm.2023.10.003
[56] Theng D, Bhoyar KK. Feature selection techniques for machine learning: a survey of more than two decades of research. Knowledge and Information Systems. 2024;66(3):1575-1637. doi: https://doi.org/10.1007/s10115-023-02010-5
[57] Breiman L. Random forests. Machine Learning. 2001;45(1):5-32. doi: https://doi.org/10.1023/A:1010933404324
[58] Cortes C, Vapnik V. Support-vector networks. Machine Learning. 1995;20(3):273-297. doi: https://doi.org/10.1007/BF00994018
[59] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8):1735-1780. doi: https://doi.org/10.1162/neco.1997.9.8.1735
[60] Welch P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. Institute of Electrical and Electronics Engineers Transactions on Audio and Electroacoustics. 1967;15(2):70-73. doi: https://doi.org/10.1109/TAU.1967.1161901
[61] Testas A. Random forest classification with scikit-learn and PySpark. In: Distributed Machine Learning with PySpark: Migrating Effortlessly from Pandas and Scikit-Learn; 2023. p. 243-258. Berkeley, CA: Apress. doi: https://doi.org/10.1007/978-1-4842-9751-3_9
[62] Rane N, Choudhary SP, Rane J. Ensemble deep learning and machine learning: applications, opportunities, challenges, and future directions. Studies in Medical and Health Sciences. 2024;1(2):18-41. doi: https://doi.org/10.48185/smhs.v1i2.1225
[63] Zhou L, Zhu Q, Chen Q, Wang P, Huang H. Predicting hospital outpatient volume using XGBoost: a machine learning approach. Scientific Reports. 2025;15(1):17028. doi: https://doi.org/10.1038/s41598-025-01265-y
[64] Moniruzzaman M, Okilly AH, Choi S, Baek J, Mannan TI, Islam Z. A comprehensive study of machine learning algorithms for GPU based real-time monitoring and lifetime prediction of IGBTs. In: 2024 Institute of Electrical and Electronics Engineers Applied Power Electronics Conference and Exposition (APEC); 2024. p. 2678-2684. Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/APEC48139.2024.10509167
[65] Lu B, Huang H, Wu Z, Zhang T, Gu Y, Wang F, Shu Z. Utilizing LightGBM to explore the characterization of PM2.5 emission patterns from broadleaf tree combustion in Northeastern China. Forests. 2025;16(5):836. doi: https://doi.org/10.3390/f16050836
[66] Zhang S, Duan L, Jiang H. Accelerating Gaussian beam tracing method with dynamic parallelism on graphics processing units. Computer Physics Communications. 2025;315:109722. doi: https://doi.org/10.1016/j.cpc.2025.109722
[67] Sekar PC, Shnain AH, Rani MJ, Durgadevi G, Chethana R. A support vector machine with elastic net regularization and radial basis function based spectrum sensing for cognitive radio networks. In: 2024 First International Conference on Software, Systems and Information Technology (SSITCON); 2024. p. 1-5. Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/SSITCON62437.2024.10796356
[68] Zhang Y, Peng Y, Sun J, Gui G, Lin Y, Mao S. GPU-free specific emitter identification using signal feature embedded broad learning. Institute of Electrical and Electronics Engineers Internet of Things Journal. 2023;10(14):13028-13039. doi: https://doi.org/10.1109/JIOT.2023.3257479
[69] Wielgosz M, Skoczeń A, Mertik M. Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2017;867:40-50. doi: https://doi.org/10.1016/j.nima.2017.06.020
[70] Pakki KR, Repala ESC, Eluri DB. Performance review of deep learning models on different GPU systems. In: 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI); 2025. p. 1151-1158. Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/ICMSCI62561.2025.10894456
[71] Sinha BB, Ahsan M, Dhanalakshmi R. LightGBM empowered by whale optimization for thyroid disease detection. International Journal of Information Technology. 2023;15(4):2053-2062. doi: https://doi.org/10.1007/s41870-023-01261-3
[72] Yuniawan F, Mandala S. LightGBM-based ensemble boosting for detection and anatomical localization of myocardial infarction using 12-lead ECG morphological features. In: 2025 Institute of Electrical and Electronics Engineers International Symposium on Future Telecommunication Technologies (SOFTT); 2025. p. 105-111. Institute of Electrical and Electronics Engineers. doi: https://doi.org/10.1109/SOFTT67007.2025.11213071
[73] Donepudi S, Nakka R, Thota KK, Ajmeera M, Praveen SP, Sindhura S. Enhancing person-centric health care for diabetes prediction: a comparative study of LightGBM, XGBoost, and hybrid LIGB model. In: Enabling Person-Centric Healthcare Using Ambient Assistive Technology, Volume 2: Personalized and Patient-Centric Healthcare Services in AAT; 2025. p. 127-155. Cham: Springer Nature Switzerland. doi: https://doi.org/10.1007/978-3-031-82516-3_7
[74] Sow TH, Mehdi A. Enhancing IDS performance through a comparative analysis of random forest, XGBoost, and deep neural networks. Machine Learning with Applications. 2025;22:100738. doi: https://doi.org/10.1016/j.mlwa.2025.100738
[75] Sun Y, Wang H, Xue B, Jin Y, Yen GG, Zhang M. Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor. Institute of Electrical and Electronics Engineers Transactions on Evolutionary Computation. 2019;24(2):350-364. doi: https://doi.org/10.1109/TEVC.2019.2924461
[76] Wen Z, Shi J, He B, Chen J, Chen Y. Efficient multi-class probabilistic SVMs on GPUs. Institute of Electrical and Electronics Engineers Transactions on Knowledge and Data Engineering. 2018;31(9):1693-1706. doi: https://doi.org/10.1109/TKDE.2018.2866097
[77] Omer SZ, Hashim F, Sali A, Ahmad FA. Binary classification of low-rate DoS attacks using long short-term memory feed-forward (LSTM-FF) intrusion detection system (IDS). Engineering Science and Technology, an International Journal. 2025;66:102049. doi: https://doi.org/10.1016/j.jestch.2025.102049
[78] Dai Z, Huang W. Improving energy management practices through accurate building energy consumption prediction: analyzing the performance of LightGBM, RF, and XGBoost models with advanced optimization strategies. Electrical Engineering. 2025;107(9):12583-12605. doi: https://doi.org/10.1007/s00202-025-03167-8
[79] Shehadeh A, Alshboul O, Al Mamlook RE, Hamedat O. Machine learning models for predicting the residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction. 2021;129:103827. doi: https://doi.org/10.1016/j.autcon.2021.103827
[80] Kilinc HC, Apak S, Ozkan F, Ergin ME, Yurtsever A. Multimodal fusion of optimized GRU–LSTM with self-attention layer for hydrological time series forecasting. Water Resources Management. 2024;38(15):6045-6062. doi: https://doi.org/10.1007/s11269-024-03943-4
[81] Kocer A. Prediction of the higher heating values of biomass using machine learning methods based on proximate and ultimate analysis. Journal of Mechanical Science and Technology. 2024;38(3):1569-1574. doi: https://doi.org/10.1007/s12206-024-0247-1
[82] Xie J, Hu K, Li G, Guo Y. CNN-based driving maneuver classification using multi-sliding window fusion. Expert Systems with Applications. 2021;169:114442. doi: https://doi.org/10.1016/j.eswa.2020.114442
[83] Hossain RB, Hasan MMA, Khan MI, Ahmed M, Lin Y, Pan X. A physics-informed hybrid ensemble for robust and high-fidelity temperature forecasting in PMSMs. World Electric Vehicle Journal. 2026;17(3):133. doi: https://doi.org/10.3390/wevj17030133
[84] Zhang H, Li W, Wang G, Song F, Wen Z, Zhang H, et al. Predicting stomatal conductance of chili peppers using TPE-optimized LightGBM and SHAP feature analysis based on UAVs’ hyperspectral, thermal infrared imagery, and meteorological data. Computers and Electronics in Agriculture. 2025;231:110036. doi: https://doi.org/10.1016/j.compag.2025.110036
[85] Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics. 2019;378:686-707. doi: https://doi.org/10.1016/j.jcp.2018.10.045
[86] Sehri M, Dumond P. University of Ottawa constant and variable speed electric motor vibration and acoustic fault signature dataset. Data in Brief. 2024;53:110144. doi: https://doi.org/10.1016/j.dib.2024.110144
[87] Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research. 2006;7:1-30.
[88] Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association. 1937;32(200):675-701. doi: https://doi.org/10.1080/01621459.1937.10503522
[89] Nemenyi PB. Distribution-free multiple comparisons [PhD thesis]. Princeton (NJ): Princeton University; 1963.
[90] Kendall MG, Babington Smith B. The problem of m rankings. Annals of Mathematical Statistics. 1939;10(3):275-287. doi: https://doi.org/10.1214/aoms/1177732186
[91] Cooley JW, Tukey JW. An algorithm for the machine calculation of complex Fourier series. Mathematics of Computation. 1965;19(90):297-301. doi: https://doi.org/10.1090/S0025-5718-1965-0178586-1
[92] Saeys Y, Abeel T, Van de Peer Y. Robust feature selection using ensemble feature selection techniques. In: Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2008). Lecture Notes in Computer Science, vol 5212. Berlin: Springer; 2008. p. 313-325. doi: https://doi.org/10.1007/978-3-540-87481-2_21
[93] Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Physical Review E. 2004;69(6):066138. doi: https://doi.org/10.1103/PhysRevE.69.066138
[94] Kaufman S, Rosset S, Perlich C, Stitelman O. Leakage in data mining: formulation, detection, and avoidance. Association for Computing Machinery Transactions on Knowledge Discovery from Data. 2012;6(4):15. doi: https://doi.org/10.1145/2382577.2382579
[95] Zhang W, Peng G, Li C, Chen Y, Zhang Z. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors. 2017;17(2):425. doi: https://doi.org/10.3390/s17020425
[96] Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? In: Advances in Neural Information Processing Systems 35 (NeurIPS 2022). 2022. p. 507-520.
[97] Kapoor S, Narayanan A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns. 2023;4(9):100804. doi: https://doi.org/10.1016/j.patter.2023.100804
[98] Kirchgässner W, Wallscheid O, Böcker J. Data-driven permanent magnet temperature estimation in synchronous motors with supervised machine learning: a benchmark. Institute of Electrical and Electronics Engineers Transactions on Energy Conversion. 2021;36(3):2059-2067. doi: https://doi.org/10.1109/TEC.2021.3052546

Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Yunus Kökver
0000-0002-9864-2866
Türkiye

Neşet Koçkar
0009-0002-2250-654X
Türkiye

Ahmet Yasin Selçuk
0009-0006-9245-6030
Türkiye

Fatih Ekinci
0000-0002-1011-1105
Türkiye

Abdullah Orman ^*
0000-0002-3495-1897
Türkiye

Mehmet Serdar Güzel
0000-0002-3408-0083
Türkiye

Early Pub Date

October 22, 2026

Publication Date

-

Submission Date

June 27, 2026

Acceptance Date

October 16, 2026

Published in Issue

Year 2026 Number: Advanced Online Publication

DOI

https://doi.org/10.62189/ci.1974430

IZ

https://izlik.org/JA92HK77AB

Cite

RIS / Bibtex

APA

Kökver, Y., Koçkar, N., Selçuk, A. Y., Ekinci, F., Orman, A., & Güzel, M. S. (2026). A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation. Computers and Informatics, Advanced Online Publication, 36-57. https://doi.org/10.62189/ci.1974430

AMA

1.Kökver Y, Koçkar N, Selçuk AY, Ekinci F, Orman A, Güzel MS. A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation. Computers and Informatics. 2026;(Advanced Online Publication):36-57. doi:10.62189/ci.1974430

Chicago

Kökver, Yunus, Neşet Koçkar, Ahmet Yasin Selçuk, Fatih Ekinci, Abdullah Orman, and Mehmet Serdar Güzel. 2026. “A Unified Ensemble Learning Framework for Electric Motor Fault Diagnosis, Thermal State Prediction, and Remaining Useful Life Estimation”. Computers and Informatics, no. Advanced Online Publication: 36-57. https://doi.org/10.62189/ci.1974430.

EndNote

Kökver Y, Koçkar N, Selçuk AY, Ekinci F, Orman A, Güzel MS (October 1, 2026) A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation. Computers and Informatics Advanced Online Publication 36–57.

IEEE

[1]Y. Kökver, N. Koçkar, A. Y. Selçuk, F. Ekinci, A. Orman, and M. S. Güzel, “A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation”, Computers and Informatics, no. Advanced Online Publication, pp. 36–57, Oct. 2026, doi: 10.62189/ci.1974430.

ISNAD

Kökver, Yunus - Koçkar, Neşet - Selçuk, Ahmet Yasin - Ekinci, Fatih - Orman, Abdullah - Güzel, Mehmet Serdar. “A Unified Ensemble Learning Framework for Electric Motor Fault Diagnosis, Thermal State Prediction, and Remaining Useful Life Estimation”. Computers and Informatics. Advanced Online Publication (October 1, 2026): 36-57. https://doi.org/10.62189/ci.1974430.

JAMA

1.Kökver Y, Koçkar N, Selçuk AY, Ekinci F, Orman A, Güzel MS. A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation. Computers and Informatics. 2026;:36–57.

MLA

Kökver, Yunus, et al. “A Unified Ensemble Learning Framework for Electric Motor Fault Diagnosis, Thermal State Prediction, and Remaining Useful Life Estimation”. Computers and Informatics, no. Advanced Online Publication, Oct. 2026, pp. 36-57, doi:10.62189/ci.1974430.

Vancouver

1.Yunus Kökver, Neşet Koçkar, Ahmet Yasin Selçuk, Fatih Ekinci, Abdullah Orman, Mehmet Serdar Güzel. A unified ensemble learning framework for electric motor fault diagnosis, thermal state prediction, and remaining useful life estimation. Computers and Informatics. 2026 Oct. 1;(Advanced Online Publication):36-57. doi:10.62189/ci.1974430