Electric Motor Condition Monitoring Based on a Convolutional Neural Network
Keywords:
induction motor, broken rotor bars, current signal analysis, Recurrence Plot, Gramian Angular Fields, Markov Transition Fields, Convolutional Neural Network (CNN)
Abstract
Induction motors are among the most critical electrical equipment used in industrial processes, whose failure leads to significant economic losses. The article examines failures of the motor’s short-circuited rotor bars and discusses existing diagnostic methods based on stator current signals, and their limitations. The main challenge is the lack of data from a faulty motor under all possible operating conditions for training a neural network model. The article proposes informative features for detecting broken rotor bars and studies their behavior during non-stationary motor operation. Based on this, a data augmentation method is developed, which makes it possible to generate artificial current signals simulating motor operation under non-stationary conditions. This is achieved by introducing necessary distortions into the spectrum of preprocessed current signals, followed by applying the inverse Fourier transform. Proceeding from real and synthetic current signals, input images for a convolutional neural network (CNN) are constructed. Three time-series imaging techniques are used: Recurrence Plots, Markov Transition Fields, and Gramian Angular Fields. Based on experimental results, the three time-series imaging methods are compared, and the augmentation approach effectiveness is evaluated. The model trained on Recurrence Plots demonstrates the best accuracy improvement due to augmentation.
References
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4. Крюков О.В. Интеллектуальные датчики прогнозирования технического состояния высоковольтных электродвигателей. – Автоматизация в промышленности, 2013, № 10, с. 38–41.
5. Александров А.И., Кварацхелия Н.Г. Мониторинг и прогноз технического состояния электродвигателей. – Автоматизация в промышленности, 2020, № 10, с. 39–43.
6. Singh G.K., Saleh A. Induction Machine Drive Condition Monitoring and Diagnostic Research – a Survey. – Electric Power Systems Research, 2003, vol. 64, pp. 145–158, DOI: 10.1016/s0378-7796(02)00172-4.
7. Garcia M. et al. Efficiency Assessment of Induction Motors Operating Under Different Faulty Conditions. – IEEE Transactions on Industrial Electronics, 2019, vol. 66 (10), pp. 8072–8081, DOI: 10.1109/TIE.2018.2885719.
8. Вольдек А.И. Электрические машины. Л.: Энергия, 1978, 831 с.
9. Kliman G.B., Stein J. Methods of Motor Current Signature Analysis. – Electric Machines and Power Systems, 1992, vol. 20 (5), pp. 463–474, DOI: 10.1080/07313569208909609.
10. Tang J. et al. Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection. – Energies, 2019, vol. 13 (1), DOI: 10.3390/en13010101.
11. Henao H., Razik H., Capolino G.-A. Analytical Approach of the Stator Current Frequency Harmonics Computation for Detection of Induction Machine Rotor Faults. – IEEE Transactions on Industry Applications, 2005, vol. 41 (3), pp. 801–807, DOI: 10.1109/TIA.2005.847320.
12. Panagiotou P.A. et al. A Novel Method for Rotor Fault Diagnostics in Induction Motors Using Harmonic Isolation. – 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2023, pp. 265–271, DOI: 10.1109/SDEMPED54949.2023.10271499.
13. Sobczyk T.J., Maciolek W. Diagnostics of Rotor-Cage Faults Supported by Effects Due to Higher Mmf Harmonics. – 2003 IEEE Bologna Power Tech Conference Proceedings, 2003, vol. 2, pp. 288–292, DOI: 10.1109/PTC.2003.1304324.
14. Lee S.B. et al. Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges. – IEEE Industrial Electronics Magazine, 2020, vol. 14, No. 4, pp. 158–167, DOI: 10.1109/MIE.2020.3016138.
15. Sinitsin V. et al. Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP Model. – Mechanical Systems and Signal Processing, 2022, vol. 180, DOI: 10.1016/j.ymssp.2022.109454.
16. Wang Z., Oates T. Imaging Time-Series to Improve Classification and Imputation [Электрон. ресурс], URL: https://arxiv.org/abs/1506.00327 (дата обращения 10.05.2025).
17. Yang C. et al. Rolling Bearing Fault Diagnosis Method Based on GAF-MTF and Deep Residual Network. – 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2024, pp. 106–111, DOI: 10.1109/SDPC62810.2024.10707712.
18. Mukhopadhyay S., Kar I., Ralte Z. Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism. – 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC), 2024, pp. 129–134, DOI: 10.1109/CIEC59440.2024.10468531.
19. Eckmann J.-P., Kamphorst S.O., Ruelle D. Recurrence Plots of Dynamical Systems. – Europhysics Letters, 1987, vol. 4, pp. 973–977, DOI: 10.1209/0295-5075/4/9/004.
20. Tarek A., Sameh M. Improved Deep-Learning Rotor Fault Diagnosis Based on Multi Vibration Sensors and Recurrence Plots. – Journal of Vibration and Control, 2025, vol. 31, No. 9–10, pp. 1874–1883, DOI: 10.1177/10775463241250367.
21. Jung W. et al. Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Semi-Supervised Learning. – IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, 2022, DOI: 10.1109/IECON49645.2022.9968718.
22. Zhao P. et al. Gearbox Fault Diagnosis Method Based on Improved Semi-Supervised MTDL and GAF. – Measurement and Control, 2024, vol. 57, No. 8, pp. 1181–1193, DOI: 10.1177/00202940241230488.
23. Park C.H. et al. A Health Image for Deep Learning-Based Fault Diagnosis of a Permanent Magnet Synchronous Motor Under Variable Operating Conditions: Instantaneous Current Residual Map. – Reliability Engineering and System Safety, 2022, vol. 226, DOI: 10.1016/j.ress.2022.108715.
---
Исследование выполнено при финансовой поддержке Министерства науки и высшего образования Российской Федерации (государственное задание на выполнение фундаментальных научных исследований №FENU-2023-0010 (2023010ГЗ)).
#
1. Sarvarov A.S. et al. Vestnik MGTU im. G. I. Nosova – in Russ. (Vestnik of Nosov Magnitogorsk State Technical University), 2011, No. 3, pp. 5–8.
2. Musin A.M. Avariynye rezhimy asinhronnyh elektrodvigateley i sposoby ih zashchity (Emergency Modes of Asynchronous Electric Motors and Methods of Their Protection). M.: Kolos, 1979, 112 p.
3. After the Fall: The Costs, Causes & Consequences of Unplanned Downtime [Electron. resource], URL: https://www.techinnews.com/fall-costs-causes-consequences-unplanned-downtime/ (Access on 15.05.2025).
4. Kryukov O.V. Avtomatizatsiya v promyshlennosti – in Russ. (Automation in Industry), 2013, No. 10, pp. 38–41.
5. Aleksandrov A.I., Kvaratsheliya N.G. Avtomatizatsiya v pro-myshlennosti – in Russ. (Automation in Industry), 2020, No. 10, pp. 39–43.
6. Singh G.K., Saleh A. Induction Machine Drive Condition Monitoring and Diagnostic Research – a Survey. – Electric Power Systems Research, 2003, vol. 64, pp. 145–158, DOI: 10.1016/s0378-7796(02)00172-4.
7. Garcia M. et al. Efficiency Assessment of Induction Motors Operating Under Different Faulty Conditions. – IEEE Transactions on Industrial Electronics, 2019, vol. 66 (10), pp. 8072–8081, DOI: 10.1109/TIE.2018.2885719.
8. Vol’dek A.I. Elektricheskie mashiny (Electric Machines). L.: Energiya, 1978, 831 p.
9. Kliman G.B., Stein J. Methods of Motor Current Signature Analysis. – Electric Machines and Power Systems, 1992, vol. 20 (5), pp. 463–474, DOI: 10.1080/07313569208909609.
10. Tang J. et al. Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection. – Energies, 2019, vol. 13 (1), DOI: 10.3390/en13010101.
11. Henao H., Razik H., Capolino G.-A. Analytical Approach of the Stator Current Frequency Harmonics Computation for Detec-tion of Induction Machine Rotor Faults. – IEEE Transactions on Industry Applications, 2005, vol. 41 (3), pp. 801–807, DOI: 10.1109/TIA.2005.847320.
12. Panagiotou P.A. et al. A Novel Method for Rotor Fault Diagnostics in Induction Motors Using Harmonic Isolation. – 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2023, pp. 265–271, DOI: 10.1109/SDEMPED54949.2023.10271499.
13. Sobczyk T.J., Maciolek W. Diagnostics of Rotor-Cage Faults Supported by Effects Due to Higher Mmf Harmonics. – 2003 IEEE Bologna Power Tech Conference Proceedings, 2003, vol. 2, pp. 288–292, DOI: 10.1109/PTC.2003.1304324.
14. Lee S.B. et al. Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges. – IEEE Industrial Electronics Magazine, 2020, vol. 14, No. 4, pp. 158–167, DOI: 10.1109/MIE.2020.3016138.
15. Sinitsin V. et al. Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP Model. – Mechanical Systems and Signal Processing, 2022, vol. 180, DOI: 10.1016/j.ymssp.2022.109454.
16. Wang Z., Oates T. Imaging Time-Series to Improve Classification and Imputation [Electron. resource], URL: https://arxiv.org/abs/1506.00327 (Access on 10.05.2025).
17. Yang C. et al. Rolling Bearing Fault Diagnosis Method Based on GAF-MTF and Deep Residual Network. – 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2024, pp. 106–111, DOI: 10.1109/SDPC62810.2024.10707712.
18. Mukhopadhyay S., Kar I., Ralte Z. Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism. – 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC), 2024, pp. 129–134, DOI: 10.1109/CIEC59440.2024.10468531.
19. Eckmann J.-P., Kamphorst S.O., Ruelle D. Recurrence Plots of Dynamical Systems. – Europhysics Letters, 1987, vol. 4, pp. 973–977, DOI: 10.1209/0295-5075/4/9/004.
20. Tarek A., Sameh M. Improved Deep-Learning Rotor Fault Diagnosis Based on Multi Vibration Sensors and Recurrence Plots. – Journal of Vibration and Control, 2025, vol. 31, No. 9–10, pp. 1874–1883, DOI: 10.1177/10775463241250367.
21. Jung W. et al. Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Semi-Supervised Learning. – IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, 2022, DOI: 10.1109/IECON49645.2022.9968718.
22. Zhao P. et al. Gearbox Fault Diagnosis Method Based on Improved Semi-Supervised MTDL and GAF. – Measurement and Control, 2024, vol. 57, No. 8, pp. 1181–1193, DOI: 10.1177/00202940241230488.
23. Park C.H. et al. A Health Image for Deep Learning-Based Fault Diagnosis of a Permanent Magnet Synchronous Motor Under Variable Operating Conditions: Instantaneous Current Residual Map. – Reliability Engineering and System Safety, 2022, vol. 226, DOI: 10.1016/j.ress.2022.108715
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The research was financially supported by the Ministry of Science and Higher Education of the Russian Federation (state assignment for fundamental scientific research No. FENU-2023-0010 (2023010GZ))
2. Мусин А.М. Аварийные режимы асинхронных электродвигателей и способы их защиты. М.: Колос, 1979, 112 с.
3. After the Fall: The Costs, Causes & Consequences of Unplanned Downtime [Электрон. ресурс], URL: https://www.techinnews.com/fall-costs-causes-consequences-unplanned-downtime/ (дата обращения 15.05.2025).
4. Крюков О.В. Интеллектуальные датчики прогнозирования технического состояния высоковольтных электродвигателей. – Автоматизация в промышленности, 2013, № 10, с. 38–41.
5. Александров А.И., Кварацхелия Н.Г. Мониторинг и прогноз технического состояния электродвигателей. – Автоматизация в промышленности, 2020, № 10, с. 39–43.
6. Singh G.K., Saleh A. Induction Machine Drive Condition Monitoring and Diagnostic Research – a Survey. – Electric Power Systems Research, 2003, vol. 64, pp. 145–158, DOI: 10.1016/s0378-7796(02)00172-4.
7. Garcia M. et al. Efficiency Assessment of Induction Motors Operating Under Different Faulty Conditions. – IEEE Transactions on Industrial Electronics, 2019, vol. 66 (10), pp. 8072–8081, DOI: 10.1109/TIE.2018.2885719.
8. Вольдек А.И. Электрические машины. Л.: Энергия, 1978, 831 с.
9. Kliman G.B., Stein J. Methods of Motor Current Signature Analysis. – Electric Machines and Power Systems, 1992, vol. 20 (5), pp. 463–474, DOI: 10.1080/07313569208909609.
10. Tang J. et al. Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection. – Energies, 2019, vol. 13 (1), DOI: 10.3390/en13010101.
11. Henao H., Razik H., Capolino G.-A. Analytical Approach of the Stator Current Frequency Harmonics Computation for Detection of Induction Machine Rotor Faults. – IEEE Transactions on Industry Applications, 2005, vol. 41 (3), pp. 801–807, DOI: 10.1109/TIA.2005.847320.
12. Panagiotou P.A. et al. A Novel Method for Rotor Fault Diagnostics in Induction Motors Using Harmonic Isolation. – 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2023, pp. 265–271, DOI: 10.1109/SDEMPED54949.2023.10271499.
13. Sobczyk T.J., Maciolek W. Diagnostics of Rotor-Cage Faults Supported by Effects Due to Higher Mmf Harmonics. – 2003 IEEE Bologna Power Tech Conference Proceedings, 2003, vol. 2, pp. 288–292, DOI: 10.1109/PTC.2003.1304324.
14. Lee S.B. et al. Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges. – IEEE Industrial Electronics Magazine, 2020, vol. 14, No. 4, pp. 158–167, DOI: 10.1109/MIE.2020.3016138.
15. Sinitsin V. et al. Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP Model. – Mechanical Systems and Signal Processing, 2022, vol. 180, DOI: 10.1016/j.ymssp.2022.109454.
16. Wang Z., Oates T. Imaging Time-Series to Improve Classification and Imputation [Электрон. ресурс], URL: https://arxiv.org/abs/1506.00327 (дата обращения 10.05.2025).
17. Yang C. et al. Rolling Bearing Fault Diagnosis Method Based on GAF-MTF and Deep Residual Network. – 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2024, pp. 106–111, DOI: 10.1109/SDPC62810.2024.10707712.
18. Mukhopadhyay S., Kar I., Ralte Z. Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism. – 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC), 2024, pp. 129–134, DOI: 10.1109/CIEC59440.2024.10468531.
19. Eckmann J.-P., Kamphorst S.O., Ruelle D. Recurrence Plots of Dynamical Systems. – Europhysics Letters, 1987, vol. 4, pp. 973–977, DOI: 10.1209/0295-5075/4/9/004.
20. Tarek A., Sameh M. Improved Deep-Learning Rotor Fault Diagnosis Based on Multi Vibration Sensors and Recurrence Plots. – Journal of Vibration and Control, 2025, vol. 31, No. 9–10, pp. 1874–1883, DOI: 10.1177/10775463241250367.
21. Jung W. et al. Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Semi-Supervised Learning. – IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, 2022, DOI: 10.1109/IECON49645.2022.9968718.
22. Zhao P. et al. Gearbox Fault Diagnosis Method Based on Improved Semi-Supervised MTDL and GAF. – Measurement and Control, 2024, vol. 57, No. 8, pp. 1181–1193, DOI: 10.1177/00202940241230488.
23. Park C.H. et al. A Health Image for Deep Learning-Based Fault Diagnosis of a Permanent Magnet Synchronous Motor Under Variable Operating Conditions: Instantaneous Current Residual Map. – Reliability Engineering and System Safety, 2022, vol. 226, DOI: 10.1016/j.ress.2022.108715.
---
Исследование выполнено при финансовой поддержке Министерства науки и высшего образования Российской Федерации (государственное задание на выполнение фундаментальных научных исследований №FENU-2023-0010 (2023010ГЗ)).
#
1. Sarvarov A.S. et al. Vestnik MGTU im. G. I. Nosova – in Russ. (Vestnik of Nosov Magnitogorsk State Technical University), 2011, No. 3, pp. 5–8.
2. Musin A.M. Avariynye rezhimy asinhronnyh elektrodvigateley i sposoby ih zashchity (Emergency Modes of Asynchronous Electric Motors and Methods of Their Protection). M.: Kolos, 1979, 112 p.
3. After the Fall: The Costs, Causes & Consequences of Unplanned Downtime [Electron. resource], URL: https://www.techinnews.com/fall-costs-causes-consequences-unplanned-downtime/ (Access on 15.05.2025).
4. Kryukov O.V. Avtomatizatsiya v promyshlennosti – in Russ. (Automation in Industry), 2013, No. 10, pp. 38–41.
5. Aleksandrov A.I., Kvaratsheliya N.G. Avtomatizatsiya v pro-myshlennosti – in Russ. (Automation in Industry), 2020, No. 10, pp. 39–43.
6. Singh G.K., Saleh A. Induction Machine Drive Condition Monitoring and Diagnostic Research – a Survey. – Electric Power Systems Research, 2003, vol. 64, pp. 145–158, DOI: 10.1016/s0378-7796(02)00172-4.
7. Garcia M. et al. Efficiency Assessment of Induction Motors Operating Under Different Faulty Conditions. – IEEE Transactions on Industrial Electronics, 2019, vol. 66 (10), pp. 8072–8081, DOI: 10.1109/TIE.2018.2885719.
8. Vol’dek A.I. Elektricheskie mashiny (Electric Machines). L.: Energiya, 1978, 831 p.
9. Kliman G.B., Stein J. Methods of Motor Current Signature Analysis. – Electric Machines and Power Systems, 1992, vol. 20 (5), pp. 463–474, DOI: 10.1080/07313569208909609.
10. Tang J. et al. Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection. – Energies, 2019, vol. 13 (1), DOI: 10.3390/en13010101.
11. Henao H., Razik H., Capolino G.-A. Analytical Approach of the Stator Current Frequency Harmonics Computation for Detec-tion of Induction Machine Rotor Faults. – IEEE Transactions on Industry Applications, 2005, vol. 41 (3), pp. 801–807, DOI: 10.1109/TIA.2005.847320.
12. Panagiotou P.A. et al. A Novel Method for Rotor Fault Diagnostics in Induction Motors Using Harmonic Isolation. – 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), 2023, pp. 265–271, DOI: 10.1109/SDEMPED54949.2023.10271499.
13. Sobczyk T.J., Maciolek W. Diagnostics of Rotor-Cage Faults Supported by Effects Due to Higher Mmf Harmonics. – 2003 IEEE Bologna Power Tech Conference Proceedings, 2003, vol. 2, pp. 288–292, DOI: 10.1109/PTC.2003.1304324.
14. Lee S.B. et al. Condition Monitoring of Industrial Electric Machines: State of the Art and Future Challenges. – IEEE Industrial Electronics Magazine, 2020, vol. 14, No. 4, pp. 158–167, DOI: 10.1109/MIE.2020.3016138.
15. Sinitsin V. et al. Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP Model. – Mechanical Systems and Signal Processing, 2022, vol. 180, DOI: 10.1016/j.ymssp.2022.109454.
16. Wang Z., Oates T. Imaging Time-Series to Improve Classification and Imputation [Electron. resource], URL: https://arxiv.org/abs/1506.00327 (Access on 10.05.2025).
17. Yang C. et al. Rolling Bearing Fault Diagnosis Method Based on GAF-MTF and Deep Residual Network. – 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2024, pp. 106–111, DOI: 10.1109/SDPC62810.2024.10707712.
18. Mukhopadhyay S., Kar I., Ralte Z. Robust Fault Diagnostics of Industrial Motors: The Signal-to-Image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism. – 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC), 2024, pp. 129–134, DOI: 10.1109/CIEC59440.2024.10468531.
19. Eckmann J.-P., Kamphorst S.O., Ruelle D. Recurrence Plots of Dynamical Systems. – Europhysics Letters, 1987, vol. 4, pp. 973–977, DOI: 10.1209/0295-5075/4/9/004.
20. Tarek A., Sameh M. Improved Deep-Learning Rotor Fault Diagnosis Based on Multi Vibration Sensors and Recurrence Plots. – Journal of Vibration and Control, 2025, vol. 31, No. 9–10, pp. 1874–1883, DOI: 10.1177/10775463241250367.
21. Jung W. et al. Fault Diagnosis of Inter-turn Short Circuit in Permanent Magnet Synchronous Motors with Current Signal Imaging and Semi-Supervised Learning. – IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, 2022, DOI: 10.1109/IECON49645.2022.9968718.
22. Zhao P. et al. Gearbox Fault Diagnosis Method Based on Improved Semi-Supervised MTDL and GAF. – Measurement and Control, 2024, vol. 57, No. 8, pp. 1181–1193, DOI: 10.1177/00202940241230488.
23. Park C.H. et al. A Health Image for Deep Learning-Based Fault Diagnosis of a Permanent Magnet Synchronous Motor Under Variable Operating Conditions: Instantaneous Current Residual Map. – Reliability Engineering and System Safety, 2022, vol. 226, DOI: 10.1016/j.ress.2022.108715
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The research was financially supported by the Ministry of Science and Higher Education of the Russian Federation (state assignment for fundamental scientific research No. FENU-2023-0010 (2023010GZ))
Published
2025-05-29
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