Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor
Abstract Numerous industrial applications depend heavily on induction motors and their malfunction causes considerable financial losses. Induction motors in industrial processes have recently expanded dramatically in size, and complexity of defect identification and diagnostics for such systems has increased as well. As a result, research has concentrated on developing novel methods for the quick and accurate identification of induction motor problems.In response to these needs, this paper provides an optimised algorithm for analysing the performance of an induction motor. To analyse the operation of induction motors, an enhanced methodology on Deep Belief Networks (DBN) is introduced for recovering properties from the sensor identified vibration signals. Restricted Boltzmann Machine (RBM) is stacked utilizing multiple units of DBN model, which is then trained adopting Ant colony algorithm.An innovative method of feature extraction for autonomous fault analysis in manufacturing is provided by experimental investigations utilising vibration signals and overall accuracy of 99.8% is obtained, which therefore confirms the efficiency of DBN architecture for features extraction.
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Agah, G. R., Rahideh, A., Khodadadzadeh, H., Khoshnazar, S. M. & Hedayatikia, S. (2022). Broken rotor bar and rotor eccentricity fault detection in induction motors using a combination of discrete wavelet transform and Teager-Kaiser energy operator. IEEE Transactions on Energy Conversion, 37(3), 2199-2206. 10.1109/TEC.2022.3162394
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Chang, Y., Yan, H., Huang, W., Quan, R. & Zhang, Y. (2023). A novel starting method with reactive power compensa-tion for induction motors. IET Power Electronics, 16(3), 402-412. 10.1049/pel2.12392
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Hao, H., Fuzhou, F., Feng, J., Xun, Z., Junzhen, Z., Jun, X., Pengcheng, J., Yazhi, L., Yongchan, Q., Guanghui, S. & Caishen, C. (2022). Gear fault detection in a planetary gearbox using deep belief network. Mathematical Problems in Engineering, 2022. 10.1155/2022/9908074
Husari, F. & Seshadrinath, J. (2021). Incipient inter turn fault detection and severity evaluation in electric drive system using hybrid HCNN-SVM based model. IEEE Transactions on Industrial Informatics, 18(3), 1823-1832. 10.1109/TII.2021.3067321
Hussain, M., Memon, T. D., Hussain, I., AhmedMemon, Z. & Kumar, D. (2022). Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors. CMES-Computer Modeling in Engineering Sciences, 133(2). 10.32604/cmes.2022.020583
Jyothi, R., Holla, T., Uma, R. K. & Jayapal, R. (2021). Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor. International Journal of Power Electronics and Drive Systems, 12(2), 1205. 10.11591/ijpeds.v12.i2.pp1205-1215
Kavitha, S., Bhuvaneswari, N. S., Senthilkumar, R. & Shanker, N. R. (2022). Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data. Automatika, 63(3), 525-541. 10.1080/00051144.2022.2052533
Kumar, P. & Hati, A. S. (2021). Review on machine learning algorithm based fault detection in induction motors. Archives of Computational Methods in Engineering, 28, 1929-1940. 10.1007/s11831-020-09446-w
Le Roux, P. F. & Ngwenyama, M. K. (2022). Static and Dynamic simulation of an induction motor using Matlab/Simulink. Energies, 15(10), 3564. 10.3390/en15103564
Liang, X., Ali, M. Z. & Zhang, H., 2019. Induction motors fault diagnosis using finite element method: A review. IEEE Transactions on Industry Applications, 56(2), 1205-1217. 10.1109/TIA.2019.2958908
Lopez-Gutierrez, R., Rangel-Magdaleno, J. D. J., Morales-Perez, C. J. & García-Perez, A. (2022). Induction machine bearing fault detection using empirical wavelet transform. Shock and Vibration, 2022. 10.1155/2022/6187912
Martinez-Herrera, A. L., Ferrucho-Alvarez, E. R., Ledesma-Carrillo, L. M., Mata-Chavez, R. I., Lopez-Ramirez, M. & Ca-bal-Yepez, E. (2022). Multiple fault detection in induction motors through homogeneity and kurtosis computation. Energies, 15(4), 1541. 10.3390/en15041541
Misra, S., Kumar, S., Sayyad, S., Bongale, A., Jadhav, P., Kotecha, K., Abraham, A. & Gabralla, L. A. (2022). Fault detection in induction motor using time domain and spectral imaging-based transfer learning approach on vibration data. Sensors, 22(21), 8210. 10.3390/s22218210
Namdar, A., Samet, H., Allahbakhshi, M., Tajdinian, M. & Ghanbari, T. (2022). A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm. Measurement, 187, 110181. 10.1016/j.measurement.2021.110181
Roy, S. S., Dey, S. & Chatterjee, S. (2020). Auto correlation aided random forest classifier-based bearing fault detection framework. IEEE Sensors Journal, 20(18), 10792-10800. 10.1109/JSEN.2020.2995109
Shi, Q. & Zhang, H. (2020). Fault diagnosis of an autonomous vehicle with an improved SVM algorithm subject to un-balanced datasets. IEEE Transactions on Industrial Electronics, 68(7), 6248-6256. 10.1109/TIE.2020.2994868
Talhaoui, H., Ameid, T., Aissa, O. & Kessal, A. (2022). Wavelet packet and fuzzy logic theory for automatic fault detec-tion in induction motor. Soft Computing, 26(21), 11935-11949. 10.1007/s00500-022-07028-5
Toma, R. N., Prosvirin, A. E. & Kim, J. M., 2020. Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7), 1884. 10.3390/s20071884
Zahraoui, Y., Akherraz, M. & Ma’arif, A. (2022). A comparative study of nonlinear control schemes for induction motor operation improvement. International Journal of Robotics and Control Systems, 2(1), 1-17. 10.31763/ijrcs.v2i1.521
Zhao, X., Jia, M. & Liu, Z. (2020). Semi supervised graph convolution deep belief network for fault diagnosis of elector mechanical system with limited labeled data. IEEE Transactions on Industrial Informatics, 17(8), 5450-5460. 10.1109/TII.2020.3034189
Benninger, M., Liebschner, M. & Kreischer, C. (2023). Fault Detection of Induction Motors with Combined Modeling-and Machine-Learning-Based Framework. Energies, 16(8), 3429. 10.3390/en16083429
Chang, Y., Yan, H., Huang, W., Quan, R. & Zhang, Y. (2023). A novel starting method with reactive power compensa-tion for induction motors. IET Power Electronics, 16(3), 402-412. 10.1049/pel2.12392
Choudhary, A., Goyal, D. & Letha, S. S. (2020). Infrared thermography-based fault diagnosis of induction motor bea-rings using machine learning. IEEE Sensors Journal, 21(2), 1727-1734. 10.1109/JSEN.2020.3015868
Hao, H., Fuzhou, F., Feng, J., Xun, Z., Junzhen, Z., Jun, X., Pengcheng, J., Yazhi, L., Yongchan, Q., Guanghui, S. & Caishen, C. (2022). Gear fault detection in a planetary gearbox using deep belief network. Mathematical Problems in Engineering, 2022. 10.1155/2022/9908074
Husari, F. & Seshadrinath, J. (2021). Incipient inter turn fault detection and severity evaluation in electric drive system using hybrid HCNN-SVM based model. IEEE Transactions on Industrial Informatics, 18(3), 1823-1832. 10.1109/TII.2021.3067321
Hussain, M., Memon, T. D., Hussain, I., AhmedMemon, Z. & Kumar, D. (2022). Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors. CMES-Computer Modeling in Engineering Sciences, 133(2). 10.32604/cmes.2022.020583
Jyothi, R., Holla, T., Uma, R. K. & Jayapal, R. (2021). Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor. International Journal of Power Electronics and Drive Systems, 12(2), 1205. 10.11591/ijpeds.v12.i2.pp1205-1215
Kavitha, S., Bhuvaneswari, N. S., Senthilkumar, R. & Shanker, N. R. (2022). Magnetoresistance sensor-based rotor fault detection in induction motor using non-decimated wavelet and streaming data. Automatika, 63(3), 525-541. 10.1080/00051144.2022.2052533
Kumar, P. & Hati, A. S. (2021). Review on machine learning algorithm based fault detection in induction motors. Archives of Computational Methods in Engineering, 28, 1929-1940. 10.1007/s11831-020-09446-w
Le Roux, P. F. & Ngwenyama, M. K. (2022). Static and Dynamic simulation of an induction motor using Matlab/Simulink. Energies, 15(10), 3564. 10.3390/en15103564
Liang, X., Ali, M. Z. & Zhang, H., 2019. Induction motors fault diagnosis using finite element method: A review. IEEE Transactions on Industry Applications, 56(2), 1205-1217. 10.1109/TIA.2019.2958908
Lopez-Gutierrez, R., Rangel-Magdaleno, J. D. J., Morales-Perez, C. J. & García-Perez, A. (2022). Induction machine bearing fault detection using empirical wavelet transform. Shock and Vibration, 2022. 10.1155/2022/6187912
Martinez-Herrera, A. L., Ferrucho-Alvarez, E. R., Ledesma-Carrillo, L. M., Mata-Chavez, R. I., Lopez-Ramirez, M. & Ca-bal-Yepez, E. (2022). Multiple fault detection in induction motors through homogeneity and kurtosis computation. Energies, 15(4), 1541. 10.3390/en15041541
Misra, S., Kumar, S., Sayyad, S., Bongale, A., Jadhav, P., Kotecha, K., Abraham, A. & Gabralla, L. A. (2022). Fault detection in induction motor using time domain and spectral imaging-based transfer learning approach on vibration data. Sensors, 22(21), 8210. 10.3390/s22218210
Namdar, A., Samet, H., Allahbakhshi, M., Tajdinian, M. & Ghanbari, T. (2022). A robust stator inter-turn fault detection in induction motor utilizing Kalman filter-based algorithm. Measurement, 187, 110181. 10.1016/j.measurement.2021.110181
Roy, S. S., Dey, S. & Chatterjee, S. (2020). Auto correlation aided random forest classifier-based bearing fault detection framework. IEEE Sensors Journal, 20(18), 10792-10800. 10.1109/JSEN.2020.2995109
Shi, Q. & Zhang, H. (2020). Fault diagnosis of an autonomous vehicle with an improved SVM algorithm subject to un-balanced datasets. IEEE Transactions on Industrial Electronics, 68(7), 6248-6256. 10.1109/TIE.2020.2994868
Talhaoui, H., Ameid, T., Aissa, O. & Kessal, A. (2022). Wavelet packet and fuzzy logic theory for automatic fault detec-tion in induction motor. Soft Computing, 26(21), 11935-11949. 10.1007/s00500-022-07028-5
Toma, R. N., Prosvirin, A. E. & Kim, J. M., 2020. Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, 20(7), 1884. 10.3390/s20071884
Zahraoui, Y., Akherraz, M. & Ma’arif, A. (2022). A comparative study of nonlinear control schemes for induction motor operation improvement. International Journal of Robotics and Control Systems, 2(1), 1-17. 10.31763/ijrcs.v2i1.521
Zhao, X., Jia, M. & Liu, Z. (2020). Semi supervised graph convolution deep belief network for fault diagnosis of elector mechanical system with limited labeled data. IEEE Transactions on Industrial Informatics, 17(8), 5450-5460. 10.1109/TII.2020.3034189
Katta, P., Karunanithi, K., Raja, S. P., Ramesh, S., Prakash, S. V. J., & Joseph, D. (2024). Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31616. https://doi.org/10.14201/adcaij.31616
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