Diagnosis of a group of induction motors powered from a joint point using deep learning

• Autorzy: Zielonka Jakub , Naczyński Tymoteusz , Sułowicz Maciej

Identyfikatory

DOI

10.15199/48.2025.01.63

Warianty tytułu

PL

Diagnozowanie grupy silników indukcyjnych zasilanych ze wspólnego punktu z wykorzystaniem sieci neuronowych o uczeniu głębokim

• Języki publikacji: EN

Abstrakty

EN

The article presents the application of artificial intelligence based on deep learning algorithms for diagnostics of a group of induction motors. The group of diagnosed machines consists of four squirrel cage induction motors powered from one common point. For diagnostic purposes, similarly to the MCSA method, the stator current signal and additionally the supply voltage signal were used. In the research, the structures of convolutional neural networks - CNN were developed and then the training and testing procedure was carried out. The accuracy of the assessment obtained during the experimental tests was presented using the truth matrix.

PL

W artykule przedstawiono zastosowanie sztucznej inteligencji opartej o algorytmy uczenia głębokiego do diagnostyki grupy silników indukcyjnych. Grupa diagnozowanych maszyn składa się z czterech silników indukcyjnych klatkowych zasilanych z jednego wspólnego punktu. Do celów diagnostycznych, podobnie jak w metodzie MCSA, posłużył sygnał prądu stojana i dodatkowo sygnał napięcia zasilania. W badaniach opracowano struktury konwolucyjnych sieci neuronowych - CNN a następnie przeprowadzono procedurę treningu i testowania. Uzyskana podczas badań eksperymentalnych dokładność oceny przedstawiona została za pomocą macierzy prawdy.

Słowa kluczowe

EN

squirrel cage induction motor; electrical machine diagnostic; convolutional neural network; deep learning; MSCA

PL

silnik indukcyjny klatkowy; diagnostyka maszyny elektrycznej; konwolucyjna sieć neuronowa; uczenie głębokie; MSCA

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2025
• Tom: R. 101, nr 1
• Strony: 290--295
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Zielonka Jakub

• Cracow University of Technology, Faculty of Electrical and Computer Engineering, Department of Electrical Engineering, 24 Warszawska Street 31-155 Kraków

• https://orcid.org/0000-0003-4672-4576

autor

Naczyński Tymoteusz

• Cracow University of Technology, Faculty of Electrical and Computer Engineering, Department of Electrical Engineering, 24 Warszawska Street 31-155 Kraków

• https://orcid.org/0000-0003-0323-5393

autor

Sułowicz Maciej

• Cracow University of Technology, Faculty of Electrical and Computer Engineering, Department of Electrical Engineering, 24 Warszawska Street 31-155 Kraków

• https://orcid.org/0000-0001-6436-1110

Bibliografia

• [1] Jung J.H., Lee J.J., Kwon B.H., Online Diagnosis of Induction Motors Using MCSA, IEEE Transactions on Industrial Electronics, 53 (2006), No. 6, 1842-1852
• [2] Gangsar P., Tiwari R., Online Diagnostics of Mechanical and Electrical Faults in Induction Motor Using Multiclass Support Vector Machine Algorithms Based on Frequency Domain Vibration and Current Signals, ASME J. Risk Uncertainty Part B., 5(2019), No. 3
• [3] Seera M., Lim C.P., Ishak D., Singh H., Offline and Online Fault Detection And Diagnosis of Induction Motors Using a Hybrid Soft Computing Model, Applied Soft Computing, 13 (2013), No. 12
• [4] Niu G., Dong X., Chen Y., Motor Fault Diagnostics Based on Current Signatures: A Review, IEEE Transactions on Instrumentation and Measurement, 72 (2023), 1-19
• [5] Villalobos-Pina J., Francisco, Electric Fault Diagnosis in Induction Machines Using Motor Current Signature Analysis (MCSA). Time Series Analysis - Recent Advances, New Perspectives and Applications - IntechOpen, (2024)
• [6] Morales-Perez C., Grande-Barreto J., Rangel-Magdaleno J., Peregrina-Barreto H., Bearing Fault Detection in Induction Motors Using MCSA and Statistical Analysis, IEEE International Instrumentation and Measurement Technology Conference, (2018), 1-5
• [7] Pillay P., Xu Z., Motor Current Signature Analysis, IEEE 31th Industry Applications Conference, 1 (1996), 587-594
• [8] Sułowicz M., Duda A., Petryna J., Guziec K., The Measuring and Diagnostic System for a Non-invasive Diagnosis of the Induction Motor Rotor Condition, Maszyny Elektryczne – Zeszyty Problemowe, 2 (2017), 169-175
• [9] Petryna J., Ławrowski Z., Sułowicz M., Guziec K., Diagnosing and Thermal Verification of Induction Motors with Electromagnetic Asymmetry, Napędy i Sterowanie, 7 (2017), 144-153
• [10] Lozanov Y., Tzvetkova S., Petleshkov A., Thermal Diagnostic Model of Induction Motor Operating in Steady State Mode, 13th Electrical Engineering Faculty Conference, (2021), 1-6
• [11] Decner A., Baranski .M, Jarek T., Berhausen S., Methods of Diagnosing the Insulation of Electric Machines Windings, Energies, 15 (2022), 8465
• [12] Szymaniec S., Uszkodzenia i Diagnostyka Off-Line Stanu Izolacji Uzwojeń w Silnikach Wysokonapięciowych Indukcyjnych Klatkowych Dla Potrzeb Energetyki I Przemysłu, nowaEnergia, 1 (2017), 55
• [13] Thomson W.T., Fenger M., Current Signature Analysis to Detect Induction Motor Faults, IEEE Industry Applications Magazine, 7 (2001), No. 4, 26-34
• [14] Thomson W.T., Culbert I., Motor Current Signature Analysis for Induction Motors, Current Signature Analysis for Condition Monitoring of Cage Induction Motors: Industrial Application and Case Histories, (2017), 1-37
• [15] Capolino G.A., Antonino-Daviu J.A. Riera-Guasp J.A.M., Modern Diagnostics Techniques for Electrical Machines, IEEE Transactions on Industrial Electronics, 62 (2015), 1738–1745.
• [16] Henao H., Capolino G.-A., Fernandez-Cabanas M., Filippetti F., Bruzzese C., Strangas E., Pusca R., Estima J., Riera- Guasp M., Hedayati-Kia S., Trends in Fault Diagnosis For Electrical Machines: A Review of Diagnostic Techniques, IEEE Transactions Industrial Magazine, 8 (2014), 31–42
• [17] Sułowicz M., Diagnostyka Silników Indukcyjnych Metodami Sztucznej Inteligencji, Rozprawa Doktorska – Politechnika Krakowska, (2005), 81-87
• [18] Reddy B.K., Gajjar R., Jweeg M.J., Umate R., Pant K., Mohanraj R., Integration of Machine Learning for an Induction Motor Health Monitoring by Image Capture and Classification Method, 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, (2023), 923-926,
• [19] Sułowicz M., Borkowski D., Węgiel T., Weinreb K., Specialized Diagnostic System for Induction Motors, Przegląd Elektrotechniczny, 4 (2010), 285
• [20] Goodfellow I. Bengio Y. Courville A., Deep Learning, Massachusetts Institute of Technology Press, (2018)
• [21] Chattopadhyay P., Saha N., Delpha C., Sil J., Deep Learning in Fault Diagnosis of Induction Motor Drives, Prognostics and System Health Management Conference, (2018), 1068-1073
• [22] Husari F., Seshadrinath J., Early Stator Fault Detection and Condition Identification in Induction Motor Using Novel Deep Network, IEEE Transactions on Artificial Intelligence, 3 (2022), No. 5, 809-818
• [23] Das A.K., Das S., Pradhan A.K., Chatterjee B., Dalai S., RPCNNet: A Deep Learning Approach to Sense Minor Stator Winding Interturn Fault Severity in Induction Motor Under Variable Load Condition, IEEE Sensors Journal, 23 (2023), No. 4, 3965-3972
• [24] Jigyasu R., Shrivastava V., Singh S., Advance Deep Convolution Neural Network For Multiple Fault Diagnosis Of Induction Motor, IEEE 10th Power India International Conference, (2022), 1-6
• [25] Abdelmaksoud M., Torki M., El-Habrouk M., Elgeneidy M., Convolutional-Neural-Network-Based Multi-Signals Fault Diagnosis of Induction Motor Using Single and Multi-Channels Datasets, Alexandria Engineering Journal, 73 (2023), 231-248
• [26] Chang H.C., Wang Y.C., Shih Y.Y., Kuo C.C., Fault Diagnosis of Induction Motors with Imbalanced Data Using Deep Convolutional Generative Adversarial Network, Applied Science, 12 (2022), 4080
• [27] Ganguly B., Ray R.K., Chatterjee A., Paul S., A Deep Learning Aided Intelligent Framework for Condition Monitoring of Electrical Machinery, IEEE Devices for Integrated Circuit, (2023), 82-86
• [28] Lee J., Pack J., Lee I.S., Fault Diagnosis of Induction Motor Using Convolution Neural Network, Applied Science, 9 (2019), 2950
• [29] Hong D.T., Kang H.J., A Motor Current Signal Based Bearing Fault Diagnosis Using Deep Learning And Information Fusion, IEEE Transactions on Instrumentation and Measurement, 69 (2020), No. 6, 3325 - 3333
• [30] Jia F., Lei Y., Guo L., Lin J.X.S., A Neural Network Construcetd by Deep Learning Technique and its Application for Intelligent Fault Diagnosis of Machines, Neurocomputing, 272 (2018), 619-628
• [31] Barrera-Llanga K., Burriel-Valencia J., Sapena-Bañó Á., Martínez-Román J.A., Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors, Sensors, 23 (2023), 8196
• [32] Valtierra-Rodriguez M., Rivera-Guillen J.R., Basurto-Hurtado J.A., De-Santiago-Perez J.J., Granados-Lieberman D., Amezquita-Sanchez J.P., Convolutional Neural Network and Motor Current Signature Analysis During the Transient State for Detection of Broken Rotor Bars in Induction Motors, Sensors, 20 (2020), 3721
• [33] Sun W., Zhao R., Yan R., Shao S., Chen X., Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis, IEEE Transactions on Industrial Informatics, 13 (2017), No. 3, 1350-1359
• [34] Hsueh Y.M., Ittangihal V.R., Wu W.B., Chang H.C., Kuo C.C, Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform, Symmetry, 11 (2019), No. 10, 1212
• [35] Omar T.C., Cruz-Albarran I.A., Resendiz-Ochoa E., Salinas- Aguilar A., Morales-Hernandez L.A., Basurto-Hurtado J.A., Perez-Ramirez C.A., A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography, Machines, 11 (2023), No. 7, 752
• [36] Skowron M., Orlowska-Kowalska T., Wolkiewicz M., Kowalski C.T., Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor, Energies, 13 (2020), No. 6, 1475
• [37] Skowron M., Wolkiewicz M., Orlowska-Kowalska T., Kowalski C.T., 2019. Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors, Applied Sciences, 9 (2019), No. 4, 616