(2) S. Ravikrishna (PSG Institute of Technology and Applied Research, India)
(3) P. Rajasekar (Sri Krishna College of Technology, India)
(4) Mani Venkatesan (Dhanalakshmi Srinivasan College of Engineering, India)
*corresponding author
AbstractVariable speed induction motors controlled by variable frequency drives are used for a variety of industrial applications. Monitoring and prognostic occurrence of faults in induction motors is vital for reducing the downtime and accidents. The proposed work focuses on failures in induction motors owing to bearing misalignment and insulation failure in the stator that results in abnormal vibration and temperature rise in the motor. This research intends to improve the dependability and safety of industrial operations by identifying faults in their early stages using advanced methods such as vibration analysis and thermal monitoring. This work focuses on fault prognosis in induction motor through vibration data, which is analyzed using Daubechies orthogonal db10 wavelet transformation. The neural network algorithm optimizes the analyzed results to enable real time fault detection. The temperature of the stator is measured to estimate the expected lifetime of the insulator. The real time vibration and temperature data is measured and transferred to prognostic model build in MATLAB using ATMEGA 32 controller and the results are validated for good, allowable and not permissible conditions of motor based on ISO 10816 vibration levels for Class I motors. The improved accuracy and efficiency of real-time fault detection have the potential to reshape maintenance strategies and enhance the overall reliability of variable speed induction motors. KeywordsInduction Motor; Discrete Wavelet Transform; Artificial Neural Network; MEMS Sensor; Fault Prediction
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DOIhttps://doi.org/10.31763/ijrcs.v4i1.1252 |
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References
[1] P. Konar and P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft. Comput., vol. 11, no. 6, pp. 4203–4211, 2011, https://doi.org/10.1016/j.asoc.2011.03.014.
[2] W. Li and C. K. Mechefske, “Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods,” Journal of vibration and Control, vol. 12, no. 2, pp. 165–188, 2006, https://doi.org/10.1177/1077546306062097.
[3] N. Menasri, M. Zaoui, and A. Bouchoucha, “Detection and localization of isolated wear bearing faults of rotating machinery by spectral analysis,” World Journal of Engineering, vol. 10, no. 6, pp. 565–572, 2013, https://doi.org/10.1260/1708-5284.10.6.565.
[4] A. K. Verma, S. Nagpal, A. Desai, and R. Sudha, “An efficient neural-network model for real-time fault detection in industrial machine,” Neural Comput. Appl., vol. 33, pp. 1297–1310, 2021, https://doi.org/10.1007/s00521-020-05033-z.
[5] P. Balakrishna and U. Khan, “An autonomous electrical signature analysis-based method for faults monitoring in industrial motors,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1–8, 2021, https://doi.org/10.1109/TIM.2021.3059466.
[6] R. Sharifi and M. Ebrahimi, “Detection of stator winding faults in induction motors using three-phase current monitoring,” ISA Trans., vol. 50, no. 1, pp. 14–20, 2011, https://doi.org/10.1016/j.isatra.2010.10.008.
[7] E. T. Esfahani, S. Wang, and V. Sundararajan, “Multisensor wireless system for eccentricity and bearing fault detection in induction motors,” IEEE/ASME transactions on Mechatronics, vol. 19, no. 3, pp. 818–826, 2013, https://doi.org/10.1109/TMECH.2013.2260865.
[8] A. Rai and S. H. Upadhyay, “Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression,” Proc. Inst. Mech. Eng. C. J. Mech. Eng. Sci., vol. 232, no. 6, pp. 1118–1132, 2018, https://doi.org/10.1177/0954406217700180.
[9] K. C. D. Kompella, M. V. G. Rao, and R. S. Rao, “Bearing fault detection in a 3 phase induction motor using stator current frequency spectral subtraction with various wavelet decomposition techniques,” Ain Shams Engineering Journal, vol. 9, no. 4, pp. 2427–2439, 2018, https://doi.org/10.1016/j.asej.2017.06.002.
[10] P. C. M. Lamim Filho, J. N. Brito, V. A. D. Silva, and R. Pederiva, “Detection of electrical faults in induction motors using vibration analysis,” J. Qual. Maint. Eng., vol. 19, no. 4, pp. 364–380, 2013, https://doi.org/10.1108/JQME-06-2013-0040.
[11] P. Gangsar and R. Tiwari, “Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms,” Mech. Syst. Signal Process, vol. 94, pp. 464–481, 2017, https://doi.org/10.1016/j.ymssp.2017.03.016.
[12] M. Valtierra-Rodriguez, J. R. Rivera-Guillen, J. A. Basurto-Hurtado, J. J. De-Santiago-Perez, D. Granados-Lieberman, and J. P. Amezquita-Sanchez, “Convolutional neural network and motor current signature analysis during the transient state for detection of broken rotor bars in induction motors,” Sensors, vol. 20, no. 13, p. 3721, 2020, https://doi.org/10.3390/s20133721.
[13] R. N. Toma, A. E. Prosvirin, and J.-M. Kim, “Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers,” Sensors, vol. 20, no. 7, p. 1884, 2020, https://doi.org/10.3390/s20071884.
[14] O. AlShorman et al., “Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study,” Advances in Mechanical Engineering, vol. 13, no. 2, p. 1687814021996915, 2021, https://doi.org/10.1177/1687814021996915.
[15] S. K. Gundewar and P. V Kane, “Condition monitoring and fault diagnosis of induction motor,” Journal of Vibration Engineering & Technologies, vol. 9, pp. 643–674, 2021, https://doi.org/10.1007/s42417-020-00253-y.
[16] G. H. Bazan, P. R. Scalassara, W. Endo, A. Goedtel, W. F. Godoy, and R. H. C. Palácios, “Stator fault analysis of three-phase induction motors using information measures and artificial neural networks,” Electric Power Systems Research, vol. 143, pp. 347–356, 2017, https://doi.org/10.1016/j.epsr.2016.09.031.
[17] M. Borecki, A. Rychlik, O. Vrublevskyi, A. Olejnik, and M. L. Korwin-Pawlowski, “Method of Non-Invasive determination of wheel rim technical condition using vibration measurement and artificial neural network,” Measurement, vol. 185, p. 110050, 2021, https://doi.org/10.1016/j.measurement.2021.110050.
[18] Z. Huo, Y. Zhang, P. Francq, L. Shu, and J. Huang, “Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures,” IEEE Access, vol. 5, pp. 19442–19456, 2017, https://doi.org/10.1109/ACCESS.2017.2661967.
[19] R. Yan, R. X. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: A review with applications,” Signal Processing, vol. 96, pp. 1–15, 2014, https://doi.org/10.1016/j.sigpro.2013.04.015.
[20] B. A. Paya, I. I. Esat, and M. N. M. Badi, “Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor,” Mech. Syst. Signal Process, vol. 11, no. 5, pp. 751–765, 1997, https://doi.org/10.1006/mssp.1997.0090.
[21] K. Jayakumar and S. Thangavel, “Industrial drive fault diagnosis through vibration analysis using wavelet transform,” Journal of Vibration and control, vol. 23, no. 12, pp. 2003–2013, 2017, https://doi.org/10.1177/1077546315606602.
[22] K. S. Gaied, “Wavelet-based prognosis for fault-tolerant control of induction motor with stator and speed sensor faults,” Transactions of the Institute of Measurement and Control, vol. 37, no. 1, pp. 100–113, 2015, https://doi.org/10.1177/01423312145331.
[23] A. Almounajjed, A. K. Sahoo, M. K. Kumar, and S. K. Subudhi, “Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique,” Chinese Journal of Electrical Engineering, vol. 9, no. 1, pp. 142–157, 2023. https://doi.org/10.23919/CJEE.2023.000003.
[24] J. Ma, J. Wu, and X. Wang, “Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine,” Advances in Mechanical Engineering, vol. 10, no. 1, p. 1687814017751446, 2018, https://doi.org/10.1177/1687814017751446.
[25] S. M. K. Zaman, X. Liang, and W. Li, “Fault diagnosis for variable frequency drive-fed induction motors using wavelet packet decomposition and greedy-gradient max-cut learning,” IEEE Access, vol. 9, pp. 65490–65502, 2021, https://doi.org/10.1109/ACCESS.2021.3076149.
[26] M. H. M. Ghazali and W. Rahiman, “Vibration-based fault detection in drone using artificial intelligence,” IEEE Sens. J., vol. 22, no. 9, pp. 8439–8448, 2022, https://doi.org/10.1109/JSEN.2022.3163401.
[27] M. Kazmi, M. T. Shoaib, A. Aziz, H. R. Khan, and S. A. Qazi, “An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors.,” Computer Systems Science & Engineering, vol. 47, no. 1, 2023, https://doi.org/10.32604/csse.2023.038464.
[28] R. Miceli, Y. Gritli, A. Di Tommaso, F. Filippetti, and C. Rossi, “Vibration signature analysis for monitoring rotor broken bar in double squirrel cage induction motors based on wavelet analysis,” COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 33, no. 5, pp. 1625–1641, 2014, https://doi.org/10.1108/COMPEL-09-2013-0304.
[29] A. Yildiz, M. Akin, M. Poyraz, and G. Kirbas, “Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction,” Expert Syst. Appl., vol. 36, no. 4, pp. 7390–7399, 2009, https://doi.org/10.1016/j.eswa.2008.09.003.
[30] T. Khoualdia, A. Lakehal, Z. Chelli, K. Khoualdia, and K. Nessaib, “Optimized multi layer perceptron artificial neural network based fault diagnosis of induction motor using vibration signals,” Diagnostyka, vol. 22, 2021, https://doi.org/10.29354/diag/133091.
[31] P. Ewert, C. T. Kowalski, and T. Orlowska-Kowalska, “Low-cost monitoring and diagnosis system for rolling bearing faults of the induction motor based on neural network approach,” Electronics, vol. 9, no. 9, p. 1334, 2020, https://doi.org/10.3390/electronics9091334.
[32] C. Swarnamma, “ANN Optimized Hybrid Energy Management Control System for Electric Vehicles,” Studies in Informatics and Control, vol. 32, no. 1, pp. 101-110, 2023, https://doi.org/10.24846/v32i1y202310.
[33] R. F. Ribeiro Junior, F. A. de Almeida, and G. F. Gomes, “Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks,” Neural Comput. Appl., vol. 32, pp. 15171–15189, 2020, https://doi.org/10.1007/s00521-020-04868-w.
[34] B. Suechoey, S. Siriporananon, P. Chupun, C. Boonkhun, and C. Chompooinwai, “Performance analysis and fault classification in a large electric motor using vibration assessment technique,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 1, pp. 124–133, 2021, https://doi.org/10.22266/ijies2021.0228.13.
[35] E. L. Brancato, “Estimation of lifetime expectancies of motors,” IEEE Electrical Insulation Magazine, vol. 8, no. 3, pp. 5–13, 1992, https://doi.org/10.1109/57.139066.
[36] V. Madonna, P. Giangrande, L. Lusuardi, A. Cavallini, C. Gerada, and M. Galea, “Thermal overload and insulation aging of short duty cycle, aerospace motors,” IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 2618–2629, 2019, https://doi.org/10.1109/TIE.2019.2914630.
[37] IEEE Standards Coordinating Committee 4. IEEE Recommended Practice: General Principles for Temperature Limits in the Rating of Electrical Equipment and for the Evaluation of Electrical Insulation. IEEE, 2001, https://doi.org/10.1109/IEEESTD.2001.92768.
[38] D.-T. Hoang and H.-J. Kang, “Rolling element bearing fault diagnosis using convolutional neural network and vibration image,” Cogn. Syst. Res., vol. 53, pp. 42–50, 2019, https://doi.org/10.1016/j.cogsys.2018.03.002.
[39] Y.-M. Hsueh, V. R. Ittangihal, W.-B. Wu, H.-C. Chang, and C.-C. Kuo, “Fault diagnosis system for induction motors by CNN using empirical wavelet transform,” Symmetry (Basel), vol. 11, no. 10, p. 1212, 2019, https://doi.org/10.3390/sym11101212.
[40] H. Shao, H. Jiang, H. Zhang, W. Duan, T. Liang, and S. Wu, “Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing,” Mech. Syst. Signal Process, vol. 100, pp. 743–765, 2018, https://doi.org/10.1016/j.ymssp.2017.08.002.
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