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Fault Detection and Condition Monitoring in Induction Motors Utilizing Machine Learning Algorithms

Authors

  • Tareg Elgallai Department of Electrical-Electronics Engineering, Karabuk University, Karabuk Turkey

DOI:

10.47709/brilliance.v4i1.3539

Keywords:

Fault detection, Condition monitoring, Induction motors, Machine learning algorithms

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Abstract

Electric induction motors (IM) are considered to be a highly significant and extensively utilized category of machinery within contemporary industrial settings. Typically, powerful motors, which are frequently essential to industrial processes, are equipped with integrated condition-monitoring systems to support proactive maintenance and the identification of faults. Typically, the cost-effectiveness of such capabilities is limited for tiny motors with a power output of less than ten horsepower, given their relatively low replacement costs. Nevertheless, it is worth noting that several little motors are commonly employed by large industrial facilities, mostly to operate cooling fans or lubricating pumps that support the functionality of larger machinery. It is possible to allocate multiple small motors to a single electrical circuit, so creating a situation where a malfunction in one motor could potentially cause damage to other motors connected to the same circuit. Hence, there exists a necessity to implement condition monitoring techniques for collections of small motors. This paper presents a comprehensive overview of a continuous effort aimed at the development of a machine learning-driven solution for the identification of faults in a multitude of small electric motors.

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References

Ahmed, A. A., Alsharif, A., Triwiyanto, T., Khaleel, M., Tan, C. W., & Ayop, R. (2022). Using of neural network-based controller to obtain the effect of hub motors weight on electric vehicle ride comfort. 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE.

Ahmed, A. A., Masood, M. A., Almabrouk, A. Q., Sheggaf, Z. M., Khaleel, M. M., & Belrzaeg, M. (2021). An investigation of the effect of the hub motor weight on vehicle suspension and passenger comfort. International Journal of Mechanical and Production Engineering Research and Development, 11(5), 51–64.

Alsharif, A., Ahmed, A. A., Khaleel, M. M., Alarga, A. S. D., Jomah, O. S. M., & Alrashed, A. B. E. (2023). Stochastic method and sensitivity analysis assessments for vehicle-to-home integration based on renewable energy sources. 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE.

Alsharif, A., Ahmed, A. A., Khaleel, M. M., & Altayib, M. A. (2023). Ancillary services and energy management for electric Vehicle: Mini-review. (NAJSP), 9–12.

Alsharif, A., Ahmed, A. A., Khaleel, M. M., Daw Alarga, A. S., Jomah, O. S. M., & Imbayah, I. (2023). Comprehensive state-of-the-art of vehicle-to-grid technology. 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE.

Alsharif, A., Tan, C. W., Ayop, R., Ahmed, A. A. A., Alanssari, A., & Khaleel, M. M. (2022). Energy management strategy for Vehicle-to-grid technology integration with energy sources: Mini review. African Journal of Advanced Pure and Applied Sciences (AJAPAS), 12–16.

Alsharif, A., Tan, C. W., Ayop, R., Ahmed, A. A., & Khaleel, M. M. (2022). Electric vehicle integration with energy sources: Problem and solution review. African Journal of Advanced Pure and Applied Sciences (AJAPAS), 17–20.

Alsharif, A., Tan, C. W., Ayop, R., Al Smin, A., Ali Ahmed, A., Kuwil, F. H., & Khaleel, M. M. (2023). Impact of electric Vehicle on residential power distribution considering energy management strategy and stochastic Monte Carlo algorithm. Energies, 16(3), 1358.

AlShorman, O., Irfan, M., Saad, N., Zhen, D., Haider, N., Glowacz, A., & AlShorman, A. (2020). A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock and Vibration, 2020, 1–20.

Belrzaeg, M., Ahmed, A. A., Almabrouk, A. Q., Khaleel, M. M., Ahmed, A. A., & Almukhtar, M. (2021). Vehicle dynamics and tire models: An overview. World Journal of Advanced Research and Reviews, 12(1), 331–348.

Belrzaeg, M., Ahmed, A. A., Khaleel, M. M., Alsharif, A., Rahmah, M. M., & Alarga, A. S. D. (2023). Suspension system control process for buses with in-wheel motors. ICCEIS 2022. Basel Switzerland: MDPI.

Cerrada, M., Sánchez, R.-V., Li, C., Pacheco, F., Cabrera, D., Valente de Oliveira, J., & Vásquez, R. E. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169–196.

Choudhary, A., Goyal, D., Shimi, S. L., & Akula, A. (2019). Condition monitoring and fault diagnosis of induction motors: A review. Archives of Computational Methods in Engineering. State of the Art Reviews, 26(4), 1221–1238.

Ciabattoni, L., Ferracuti, F., Freddi, A., & Monteriu, A. (2018). Statistical spectral analysis for fault diagnosis of rotating machines. IEEE Transactions on Industrial Electronics (1982), 65(5), 4301–4310.

Cirrincione, G., Randazzo, V., Kumar, R. R., Cirrincione, M., & Pasero, E. (2020). Growing curvilinear component analysis (GCCA) for Stator fault detection in induction machines. In Neural Approaches to Dynamics of Signal Exchanges (pp. 235–244).

Diab, A. A. Z., Al-Sayed, A.-H. M., Mohammed, H. H. A., & Mohammed, Y. S. (Eds.). (2020). Literature review of induction motor drives,” in Development of Adaptive Speed Observers for Induction Machine System Stabilization. Berlin, Germany: Springer.

Diarra, M. N., Yao, Y., Li, Z., Niasse, M., Li, Y., & Zhao, H. (2022). In-situ efficiency estimation of induction motors based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA). Energies, 15(13), 4905.

Gangsar, P., & Tiwari, R. (2020). Signal-based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. Mech. Syst. Signal Process, 144(106908).

Gangsar, Purushottam, & 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), 106908.

Ghayth, A., ?im?ir, M., Khaleel, M., Ahmed, A., & Alsharif, A. (2023). An investigation of Inverse-Automatic Mechanical Transmission of EV using gear downshift approach. Int. J. Electr. Eng. and Sustain., 1–9.

Ghayth, A., Yusupov, Z., & Khaleel, M. (2023). Performance enhancement of PV array utilizing Perturb & Observe algorithm. Int. J. Electr. Eng. and Sustain., 29–37.

Glowacz, A. (2019). Detection of deterioration of three-phase induction motor using vibration signals,” Meas. Meas. Sci. Rev, 19(6), 241–249.

Gundewar, S. K., & Kane, P. V. (2020). Fuzzy FMEA analysis of induction motor and overview of diagnostic techniques to reduce risk of failure,” in Reliability, Safety and Hazard Assessment for Risk-Based Technologies. Berlin, Germany: Springer.

Irfan, M., Saad, N., Ibrahim, R., Asirvadam, V. S., Alwadie, A. S., & Sheikh, M. A. (2017). An assessment on the non-invasive methods for condition monitoring of induction motors. In Fault Diagnosis and Detection. InTech.

Jain, J. K., Ghosh, S., & Maity, S. (2020). Concurrent PI controller design for indirect vector controlled induction motor. Asian Journal of Control, 22(1), 130–142.

Khaleel, M., Abulifa, S. A., & Abulifa, A. A. (2023). Artificial intelligent techniques for identifying the cause of disturbances in the power grid. Brilliance: Research of Artificial Intelligence, 3(1), 19–31.

Khaleel, M., Ahmed, A. A., & Alsharif, A. (2023a). Artificial Intelligence in Engineering. Brilliance: Research of Artificial Intelligence, 3(1), 32–42.

Khaleel, M., Ahmed, A. A., & Alsharif, A. (2023b). Technology challenges and trends of electric motor and drive in electric vehicle. Int. J. Electr. Eng. and Sustain., 41–48.

Khaleel, M. M., Adzman, M. R., & Zali, S. M. (2021). An integrated of hydrogen fuel cell to distribution network system: Challenging and opportunity for D-STATCOM. Energies, 14(21), 7073.

Khaleel, M. M., Mohamed Ghandoori, T., Ali Ahmed, A., Alsharif, A., Ahmed Alnagrat, A. J., & Ali Abulifa, A. (2022). Impact of mechanical storage system technologies: A powerful combination to empowered the electrical grids application. 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA). IEEE.

Khaleel, M., Nassar, Y., El-Khozondar, H. J., Elmnifi, M., Rajab, Z., Yaghoubi, E., & Yaghoubi, E. (2024). Electric vehicles in China, Europe, and the United States: Current trend and market comparison. Int. J. Electr. Eng. and Sustain., 1–20.

Khaleel, M., ?i?m?i?r, M., Yusupov, Z., Yasser, N., Elkhozondar, H., & Ahmed, A. A. (2023). The role of fault detection and diagnosis in induction motors. Int. J. Electr. Eng. and Sustain., 31–40.

Khaleel, M., Yaghoubi, E., Yaghoubi, E., & Jahromi, M. Z. (2023). The role of mechanical energy storage systems based on artificial intelligence techniques in future sustainable energy systems. Int. J. Electr. Eng. and Sustain., 01–31.

Khaleel, M., Yusupov, Z., Yasser, N., & El-Khozondar, H. J. (2023). Enhancing Microgrid performance through hybrid energy storage system integration: ANFIS and GA approaches. Int. J. Electr. Eng. and Sustain., 38–48.

Kumar, R. R., Andriollo, M., Cirrincione, G., Cirrincione, M., & Tortella, A. (2022). A comprehensive review of conventional and intelligence-based approaches for the fault diagnosis and condition monitoring of induction motors. Energies, 15(23), 8938. doi:10.3390/en15238938

Sheikh, M. A., Nor, N. M., Ibrahim, T., Bakhsh, S. T., Irfan, M., & Daud, H. B. (2017). Noninvasive methods for condition monitoring and electrical fault diagnosis of induction motors. In Fault Diagnosis and Detection. InTech.

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ARTICLE Published HISTORY

Submitted Date: 2024-01-30
Accepted Date: 2024-01-31
Published Date: 2024-03-11

How to Cite

Elgallai, T. (2024). Fault Detection and Condition Monitoring in Induction Motors Utilizing Machine Learning Algorithms. Brilliance: Research of Artificial Intelligence, 4(1), 38-46. https://doi.org/10.47709/brilliance.v4i1.3539