Authors : Hakeem Issah; Asante Prince Kwabena; Boateng Kelvin Osei; Elvis Afful; Norbert Awuah; Alhassan Osumanu

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://shorturl.at/mPUgk

Scribd : https://tinyurl.com/mu3zhwm2

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV003

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : With the advancements of Industry 4.0, condition monitoring maintenance has become essential for preventing equipment failures and operational disruptions. Motor Current Signature Analysis (MCSA) is commonly utilized for condition monitoring to detect and diagnose various faults in Induction Motors (IMs). Despite its popularity, there is limited research comparing deep learning models for Induction Motor fault detection and classification with traditional approaches. This study explores the detection and classification of Induction Motor faults using three Transfer Learning (TL) models: InceptionV3, ResNet152, and VGG19. The research began by modeling a Squirrel Cage induction motor in MATLAB to simulate healthy, single- phasing, and double-phasing conditions, capturing time- domain stator current signatures (current spectrum) to identify fault characteristics. The data were then used to assess the effectiveness of the TL models in detecting and classifying motor faults. Around 500 datasets were created from these simulated conditions, labeled accordingly, and used to train and validate the TL models, each incorporating additional convolutional layers to enhance performance. Model evaluation utilized metrics such as the multiclass confusion matrix, precision, recall, and F1-score across various fault scenarios. Results indicate that stator current signatures can effectively reveal individual faults, with ResNet152 outperforming the other models in classification accuracy. These findings highlight that applying transfer learning techniques with a limited amount of current signature data can support predictive maintenance in industrial settings, potentially reducing costly equipment shutdowns and disruptions in production.

Keywords : Convolutional Neural Network, Transfer Learning, Simulink.

References :

1. T. G. Calva, D. M. Sotelo, V. F. Calero, R. R. Tronsoco “Early Detection of Faults in Induction Motors—A Review”, Energies, pp. 2
2. A. A. Qazi, J. Daudpoto, S. A. Shaikh “Comparison of Fault Detection Techniques for Induction Motors”, International Journal of Computer Applications (0975 – 8887), Volume 183, pp. 1
3. Sabir, H.; Ouassaid, M.; Ngote, N. An experimental method for diagnostic of incipient broken rotor bar fault in induction machines. Heliyon 2022, 8, e09136.
4. Siddique, A.; Yadava, G.; Singh, B. A review of stator fault monitoring techniques of induction motors. IEEE Trans. Energy Convers. 2005, 20, 106–114.
5. Sabir, H., Ouassaid, M., Ngote, N. (2022). An experimental method for diagnostic of incipient broken rotor bar fault in induction machines. Heliyon, 8(3).
6. Khanjani, M., Ezoji, M. (2021). Electrical fault detection in three-phase induction motor using deep network-based features of thermograms. Measurement, 173, 108622.
7. Gundewar, S., Kane, P., Andhare, A. (2022). Detection of broken rotor bar fault in an induction motor using convolution neural network. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 16(2), JAMDSM0020JAMDSM0020.
8. Huang, Z., Wang, T., Liu, W., Valencia-Cabrera, L., Perez-Jim´ enez, M. J., Li, P. (2021). A fault analysis method´ for three-phase induction motors based on spiking neural P systems. Complexity, 2021(1), 2087027.
9. Almounajjed, A., Sahoo, A. K., Kumar, M. K. (2021). Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis. Measurement, 182, 109780.
10. Lee, J. H., Pack, J. H., Lee, I. S. (2019). Fault diagnosis of induction motor using convolutional neural network. Applied Sciences, 9(15), 2950.
11. Barcelos, A. S., Cardoso, A. J. M. (2021). Current based bearing fault diagnosis using deep learning algorithms. Energies, 14(9), 2509.
12. Hussein, A. M., Obed, A. A., Zubo, R. H., Al-Yasir, Y. I., Saleh, A. L., Fadhel, H., ... Abd-Alhameed, R. A. (2022). Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach. Electronics, 11(8), 1253.
13. Garcia-Calva, T. A., Morinigo-Sotelo, D., Fernandez Cavero, V., Garcia-Perez, A., Romero-Troncoso, R. D. J. (2021). Early detection of broken rotor bars in inverter-fed induction motors using speed analysis of startup transients. Energies, 14(5), 1469.
14. Gyftakis, K. N., Marques-Cardoso, A. J. (2019, October). Reliable detection of very low severity level stator inter-turn faults in induction motors. In IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society (Vol. 1, pp. 1290-1295). IEEE.
15. Hussain, M., Memon, T. D., Hussain, I., Ahmed Memon, Z., Kumar, D. (2022). Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors. CMES-Computer Modeling in Engineering Sciences, 133(2).
16. Morales-Perez, C., Rangel-Magdaleno, J., PeregrinaBarreto, H., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M. (2018). Incipient broken rotor bar detection in induction motors using vibration signals and the orthogonal matching pursuit algorithm. IEEE Transactions on Instrumentation and Measurement, 67(9), 2058-2068.