An AutoML-based comparative evaluation of machine learning methods for detection of eccentricity faults in induction motors by using vibration signals

• Autorzy: Irgat Eyüp , Ünsal Abdurrahman

Identyfikatory

DOI

10.24425/mms.2024.152052

• Języki publikacji: EN

Abstrakty

EN

Induction motors (IMs) are the most widely used electrical machines in industrial applications. However, they are subject to various mechanical and electrical faults. Eccentricity faults are among the common mechanical faults of IMs. This study compares the performance of four commonly used machine learning (ML) methods, including k-nearest neighbours (k-NN), decision tree (DT), support vector machine (SVM), and random forest (RF) along with the statistical features in detecting eccentricity faults of IMs with an automated machine learning (AutoML) model. The aim of using AutoML in this study is to fully automate the process of detection of eccentricity faults of IMs by selecting the classifier with the highest accuracy rate and shortest computation time along with the most effective feature(s). The eccentricity fault analysed in this study was experimentally implemented in the laboratory. Three-axis vibration signals were collected for healthy and eccentricity-faulty IMs. In the proposed study the three-axis vibration signals are pre-processed to determine the statistical features that are used as input to the ML methods. The proposed study offers the best ML method among the four studied algorithms and the need for expert knowledge of ML and eccentricity fault detection. The proposed AutoML model offers the DT method along with the z-axis rms feature for the highest accuracy rate and the shortest computation time in detecting the eccentricity fault.

Słowa kluczowe

EN

induction motors; eccentricity faults; machine learning techniques; fault detection; vibration analysis; AutoML

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2024
• Tom: Vol. 31, nr 4
• Strony: 831--848
• Opis fizyczny: Bibliogr. 39 poz., rys., tab., wykr., wzory

Twórcy

autor

Irgat Eyüp

• Department of Kutahya Technical Sciences Vocational School, Kutahya Dumlupinar University, Evliya Celebi Campus, 43100 Kutahya, Turkey

autor

Ünsal Abdurrahman

• Department of Electrical Electronics Engineering, Kutahya Dumlupinar University, Evliya Celebi Campus, 43100 Kutahya, Turkey

Bibliografia

• [1] Gangsar, P., & Tiwari, R. (2020). Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mechanical Systems and Signal Processing, 144, 106908. https://doi.org/10.1016/j.ymssp.2020.106908
• [2] Bessous, N. (2020). Reliability surveys of fault distributions in rotating electrical machines: - Case study of fault detections in IMs - 2020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), 535-543. https://doi.org/10.1109/CCSSP49278.2020.9151672
• [3] Bonnett, A., & Yung, C. (2008). Increased efficiency versus increased reliability. IEEE Industry Applications Magazine, 14 (1), 29-36. https://doi.org/10.1109/MIA.2007.909802
• [4] Liang, X., Ali, M.Z., & Zhang, H. (2020). Induction motors fault diagnosis using finite element method: A review. IEEE Transactions on Industry Applications, 56(2), 1205-1217. https://doi.org/10.1109/TIA.2019.2958908
• [5] Liu, Z., Zhang, P., He, S., & Huang, J. (2021). A review of modeling and diagnostic techniques for eccentricity fault in electric machines. Energies, 14 (14), 4296. https://doi.org/10.3390/en14144296
• [6] Alimardani, R., Rahideh, A., & Hedayati Kia, S. (2024). Mixed eccentricity fault detection for induction motors based on time synchronous averaging of vibration signals. IEEE Transactions on Industrial Electronics, 71 (3), 3173-3181. https://doi.org/10.1109/TIE.2023.3266589
• [7] Wang, B., Lin, C., Inoue, H., & Kanemaru, M. (2022). Topological data analysis for electric motor eccentricity fault detection. IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society, 1-6. https://doi.org/10.1109/IECON49645.2022.9968912
• [8] Masoumi, Z., Moaveni, B., Mousavi Gazafrudi, S.M., & Faiz, J. (2022). Air-gap eccentricity fault detection, isolation, and estimation for synchronous generators based on eigenvalues analysis. ISA Transactions, 131, 489-500. https://doi.org/10.1016/j.isatra.2022.04.038
• [9] Amanuel, T., Ghirmay, A., Ghebremeskel, H., Ghebrehiwet, R., & Bahlibi, W. (2021). Design of vibration frequency method with fine-tuned factor for fault detection of three phase induction motor. Journal of Innovative Image Processing, 3 (1), 52-65. https://doi.org/10.36548/jiip.2021.1.005
• [10] Yu, G. (2020). A concentrated time-frequency analysis tool for bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 69(2), 371-381. https://doi.org/10.1109/TIM.2019.2901514
• [11] Ahsan, M., & Salah, M.M. (2023). Similarity index of the STFT-based health diagnosis of variable speed rotating machines. Intelligent Systems with Applications, 20, 200270. https://doi.org/10.1016/j.iswa.2023.200270
• [12] Ehya, Hossein., Nysveen, Arne., & Antonino-Daviu, Jose. A. (2021). Static, dynamic and mixed eccentricity faults detection of synchronous generators based on advanced pattern recognition algorithm. 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 173-179. https://doi.org/10.1109/SDEMPED51010.2021.9605488
• [13] Liu, Z., Song, Y., Liu, J., Zhang, L., Huang, B., Wu, D., & Liu, J. (2023). Modulation characteristics of multi-physical fields induced by air-gap eccentricity faults for typical rotating machine. Alexandria Engineering Journal, 83, 122-133. https://doi.org/10.1016/j.aej.2023.10.044
• [14] Neupane, D., & Seok, J. (2020). Bearing fault detection and diagnosis using Case Western Reserve University dataset with deep learning approaches: A review. IEEE Access, 8, 93155-93178. https://doi.org/10.1109/ACCESS.2020.2990528
• [15] Bousseksou, R., Bessous, N., Zarour, L., Sbaa, S., Pusca, R., Raphael, R., Rezaoui, M.M., Merzouk, I., & Borni, A. (2022). Detailed analysis of rotor eccentricity fault signatures in induction motors using DWT-FFT technique. 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD), 74-79. https://doi.org/10.1109/SSD54932.2022.9955688
• [16] Boudiaf, R., Abdelkarim, B., & Issam, H. (2024). Bearing fault diagnosis in induction motor using continuous wavelet transform and convolutional neural networks. International Journal of Power Electronics and Drive Systems (IJPEDS), 15 (1), 591. https://doi.org/10.11591/ijpeds.v15.i1.pp591-602
• [17] Wang, J., Du, G., Zhu, Z., Shen, C., & He, Q. (2020). Fault diagnosis of rotating machines based on the EMD manifold. Mechanical Systems and Signal Processing, 135, 106443. https://doi.org/10.1016/j.ymssp.2019.106443
• [18] Kwon, W., Lee, J., Choi, S., & Kim, N. (2024). Empirical mode decomposition and Hilbert-Huang transform-based eccentricity fault detection and classification with demagnetization in 120 kW interior permanent magnet synchronous motors. Expert Systems with Applications, 241, 122515. https://doi.org/10.1016/j.eswa.2023.122515
• [19] Nishat Toma, R., & Kim, J.-M. (2020). Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Applied Sciences, 10 (15), 5251. https://doi.org/10.3390/app10155251
• [20] Borja, C.A., Tisado, K.J., & Ostia, C. (2022). Fault diagnosis of a brushless DC motor using k-nearest neighbor classification technique with discrete wavelet transform feature extraction. 2022 14th International Conference on Computer and Automation Engineering (ICCAE), 122-126. https://doi.org/10.1109/ICCAE55086.2022.9762425
• [21] Tang, H., Lu, S., Qian, G., Ding, J., Liu, Y., & Wang, Q. (2021). IoT-based signal enhancement and compression method for efficient motor bearing fault diagnosis. IEEE Sensors Journal, 21 (2), 1820-1828. https://doi.org/10.1109/JSEN.2020.3017768
• [22] Martin-Diaz, I., Morinigo-Sotelo, D., Duque-Perez, O., & Romero-Troncoso, R.J. (2018). An experimental comparative evaluation of machine learning techniques for motor fault diagnosis under various operating conditions. IEEE Transactions on Industry Applications, 54 (3), 2215-2224. https://doi.org/10.1109/TIA.2018.2801863
• [23] Kudelina, K., Vaimann, T., Asad, B., Rassõlkin, A., Kallaste, A., & Demidova, G. (2021). Trends and challenges in intelligent condition monitoring of electrical machines using machine learning. Applied Sciences, 11 (6), 2761. https://doi.org/10.3390/app11062761
• [24] Irgat, E., Unsal, A., & Canseven, H.T. (2021). Detection of eccentricity faults of induction motors based on decision trees. 2021 13th International Conference on Electrical and Electronics Engineering (ELECO), 435-439. https://doi.org/10.23919/ELECO54474.2021.9677809
• [25] Yatsugi, K., Pandarakone, S.E., Mizuno, Y., & Nakamura, H. (2023). Common Diagnosis Approach to Three-Class Induction Motor Faults Using Stator Current Feature and Support Vector Machine. IEEE Access, 11, 24945-24952. https://doi.org/10.1109/ACCESS.2023.3254914
• [26] Roy, S.S., Dey, S., & Chatterjee, S. (2020). Autocorrelation aided random forest classifier-based bearing fault detection framework. IEEE Sensors Journal, 20 (18), 10792-10800. https://doi.org/10.1109/JSEN.2020.2995109
• [27] Stief, A., Ottewill, J.R., Baranowski, J., & Orkisz, M. (2019). A PCA and two-stage Bayesian sensor fusion approach for diagnosing electrical and mechanical faults in induction motors. IEEE Transactions on Industrial Electronics, 66 (12), 9510-9520. https://doi.org/10.1109/TIE.2019.2891453
• [28] Chen, Y., Rao, M., Feng, K., & Niu, G. (2023). Modified Varying Index Coefficient Autoregression Model for Representation of the Nonstationary Vibration from a Planetary Gearbox. IEEE Transactions on Instrumentation and Measurement, 72, 1-12. https://doi.org/10.1109/TIM.2023.3259048
• [29] Chen, Y., Rao, M., Feng, K., & Zuo, M.J. (2022). Physics-Informed LSTM hyperparameters selection for gearbox fault detection. Mechanical Systems and Signal Processing, 171, 108907. https://doi.org/10.1016/j.ymssp.2022.108907
• [30] Mohd Amiruddin, A.A.A., Zabiri, H., Taqvi, S.A.A., & Tufa, L.D. (2020). Neural network applications in fault diagnosis and detection: An overview of implementations in engineering-related systems. Neural Computing and Applications, 32 (2), 447-472. https://doi.org/10.1007/s00521-018-3911-5
• [31] Zhukovskiy, Y., Buldysko, A., & Revin, I. (2023). Induction motor bearing fault diagnosis based on singular value decomposition of the stator current. Energies, 16 (8), 3303. https://doi.org/10.3390/en16083303
• [32] Soofi, A.A., & Awan, A. (2017). Classification techniques in machine learning: Applications and issues. Journal of Basic & Applied Sciences, 13, 459-465. https://doi.org/10.6000/1927-5129.2017.13.76
• [33] da Silva, R.R., & Giesbrecht, M. (2021). Detection of broken rotor bars in induction motors through the k-NN algorithm combined with a deterministic-stochastic subspace method for system identification. IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society, 1-6. https://doi.org/10.1109/IECON48115.2021.9589128
• [34] Tangirala, S. (2020). Evaluating the impact of Gini index and information gain on classification using decision tree classifier algorithm*. International Journal of Advanced Computer Science and Applications, 11 (2). https://doi.org/10.14569/IJACSA.2020.0110277
• [35] Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access, 6, 52843-52856. https://doi.org/10.1109/ACCESS.2018.2869577
• [36] Mahami, A., Rahmoune, C., Bettahar, T., & Benazzouz, D. (2021). Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques. Advances in Mechanical Engineering, 13 (11), 168781402110609. https://doi.org/10.1177/16878140211060956
• [37] Karampasoglou, D., Bonet-Jara, J., & Gyftakis, K. (2023). Static, dynamic and mixed eccentricity fault detection using MCSA and stray flux monitoring via finite element analysis. 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 272-278. https://doi.org/10.1109/SDEMPED54949.2023.10271409
• [38] Ünsal, A. (2019). Asenkron motorlar arızalarının tespiti ve entropi analizi ile arıza şiddetinin belirlenmesi (Detection of induction motor faults and determination of fault severity by entropy analysis). TÜBİTAK. https://search.trdizin.gov.tr/tr/yayin/ara?q=116E302 (2023) (in Turkish). Accessed 26 June 2023
• [39] Kabul, A., & Ünsal, A. (2022). Diagnosis of multiple faults of an induction motor based on Hilbert envelope analysis. Metrology and Measurement Systems, 29 (1), 191-205. https://doi.org/10.24425/mms.2022.138541

Uwagi

This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with grant number 116E302.