Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions
DOI:
https://doi.org/10.20998/2074-272X.2024.3.04Keywords:
diagnosis, short circuit, broken bars, induction motor, discrete wavelet transform, artificial neural network, indirect field oriented controlAbstract
Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.
References
Halder S., Bhat S., Zychma D., Sowa P. Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor – A Review. Energies, 2022, vol. 15, no. 22, art. no. 8569. doi: https://doi.org/10.3390/en15228569.
Babaa F., Bennis O. An accurate inter-turn short circuit faults model dedicated to induction motors. International Journal of Electrical and Computer Engineering (IJECE), 2021, vol. 11, no. 1, pp. 9-16. doi: https://doi.org/10.11591/ijece.v11i1.pp9-16.
Adouni A., Marques Cardoso A.J. Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults. Machines, 2020, vol. 9, no. 1, art. no. 2. https://doi.org/10.3390/machines9010002.
Tomczyk M., Mielnik R., Plichta A., Goldasz I., Sułowicz M. Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing. Energies, 2021, vol. 15, no. 1, art. no. 117. doi: https://doi.org/10.3390/en15010117.
M’hamed B., Djamel T., Bessedik S.A., Mohamed-Fouad B. Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor. Diagnostyka, 2020, vol. 21, no. 4, pp. 35-41. doi: https://doi.org/10.29354/diag/130283.
Bednarz S., Dybkowski M. Induction motor windings faults detection using flux-error based MRAS estimators. Diagnostyka, 2019, vol. 20, no. 2, pp. 87-96. doi: https://doi.org/10.29354/diag/109092.
Filho P.C.M.L., Santos D.C., Batista F.B., Baccarini L.M.R. Axial Stray Flux Sensor Proposal for Three-Phase Induction Motor Fault Monitoring by Means of Orbital Analysis. IEEE Sensors Journal, 2020, vol. 20, no. 20, pp. 12317-12325. doi: https://doi.org/10.1109/JSEN.2020.2999547.
Henriques K., Laadjal K., Cardoso A.J.M. Inter-Turn Short-Circuit Fault Detection in Synchronous Reluctance Machines, Based on Current Analysis. Engineering Proceedings, 2022, vol. 24, no. 1, art. no. 23. doi: https://doi.org/10.3390/IECMA2022-12884.
Alloui A., Laadjal K., Sahraoui M., Marques Cardoso A.J. Online Interturn Short-Circuit Fault Diagnosis in Induction Motors Operating Under Unbalanced Supply Voltage and Load Variations, Using the STLSP Technique. IEEE Transactions on Industrial Electronics, 2023, vol. 70, no. 3, pp. 3080-3089. doi: https://doi.org/10.1109/TIE.2022.3172751.
Ouamara D., Boukhnifer M., Chaibet A., Maidi A. Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis. Diagnostyka, 2023, vol. 24, no. 1, pp. 1-10. doi: https://doi.org/10.29354/diag/161309.
Sakhara S., Brahimi M., Nacib L., Layadi T.M. Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines. Electrical Engineering & Electromechanics, 2023, no. 3, pp. 21-27. doi: https://doi.org/10.20998/2074-272X.2023.3.03.
Abu Ibaid O.Z.I., Belhamdi S., Abid M., Chakroune S., Mouassa S., Al-Sagar Z.S. Wavelet packet analysis for rotor bar breakage in an inverter induction motor. Electrical Engineering & Electromechanics, 2023, no. 3, pp. 3-11. doi: https://doi.org/10.20998/2074-272X.2023.3.01.
Defdaf M., Berrabah F., Chebabhi A., Cherif B.D.E. A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis. International Transactions on Electrical Energy Systems, 2021, vol. 31, no. 4, art. no. e12807. doi: https://doi.org/10.1002/2050-7038.12807.
Talhaoui H., Ameid T., Kessal A. Energy eigenvalues and neural network analysis for broken bars fault diagnosis in induction machine under variable load: experimental study. Journal of Ambient Intelligence and Humanized Computing, 2022, vol. 13, no. 5, pp. 2651-2665. doi: https://doi.org/10.1007/s12652-021-03172-2.
Senthil Kumar R., Gerald Christopher Raj I., Alhamrouni I., Saravanan S., Prabaharan N., Ishwarya S., Gokdag M., Salem M. A combined HT and ANN based early broken bar fault diagnosis approach for IFOC fed induction motor drive. Alexandria Engineering Journal, 2023, vol. 66, pp. 15-30. doi: https://doi.org/10.1016/j.aej.2022.12.010.
Ramu S.K., Vairavasundaram I., Aljafari B., Kareri T. Rotor Bar Fault Diagnosis in Indirect Field–Oriented Control-Fed Induction Motor Drive Using Hilbert Transform, Discrete Wavelet Transform, and Energy Eigenvalue Computation. Machines, 2023, vol. 11, no. 7, art. no. 711. doi: https://doi.org/10.3390/machines11070711.
Sabir H., Ouassaid M., Ngote N. An experimental method for diagnostic of incipient broken rotor bar fault in induction machines. Heliyon, 2022, vol. 8, no. 3, art. no. e09136. doi: https://doi.org/10.1016/j.heliyon.2022.e09136.
Bessam B., Menacer A., Boumehraz M., Cherif H. Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. International Journal of System Assurance Engineering and Management, 2017, vol. 8, no. S1, pp. 478-488. doi: https://doi.org/10.1007/s13198-015-0400-4.
Almounajjed A., Sahoo A.K., Kumar M.K. Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis. Measurement, 2021, vol. 182, art. no. 109780. doi: https://doi.org/10.1016/j.measurement.2021.109780.
Kim M.-C., Lee J.-H., Wang D.-H., Lee I.-S. Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods. Sensors, 2023, vol. 23, no. 5, art. no. 2585. doi: https://doi.org/10.3390/s23052585.
Hussein A.M., Obed A.A., Zubo R.H.A., Al-Yasir Y.I.A., Saleh A.L., Fadhel H., Sheikh-Akbari A., Mokryani G., Abd-Alhameed R.A. Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach. Electronics, 2022, vol. 11, no. 8, art. no. 1253. doi: https://doi.org/10.3390/electronics11081253.
Garcia-Calva T.A., Morinigo-Sotelo D., Fernandez-Cavero V., Garcia-Perez A., Romero-Troncoso R. de J. Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies, 2021, vol. 14, no. 5, art. no. 1469. doi: https://doi.org/10.3390/en14051469.
Harzelli I., Menacer A., Ameid T. A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor. Journal of Ambient Intelligence and Humanized Computing, 2020, vol. 11, no. 6, pp. 2519-2538. doi: https://doi.org/10.1007/s12652-019-01307-0.
Jankowska K., Dybkowski M. Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks. Designs, 2022, vol. 6, no. 1, art. no. 18. doi: https://doi.org/10.3390/designs6010018.
Talhaoui H., Ameid T., Aissa O., Kessal A. Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor. Soft Computing, 2022, vol. 26, no. 21, pp. 11935-11949. doi: https://doi.org/10.1007/s00500-022-07028-5.
Aib A., Khodja D.E., Chakroune S., Rahali H. Fuzzy current analysis-based fault diagnostic of induction motor using hardware co-simulation with field programmable gate array. Electrical Engineering & Electromechanics, 2023, no. 6, pp. 3-9. doi: https://doi.org/10.20998/2074-272X.2023.6.01.
Mabrouk Y.A., Mokhtari B., Allaoui T. Frequency analysis of stator currents of an induction motor controlled by direct torque control associated with a fuzzy flux estimator. Electrical Engineering & Electromechanics, 2023, no. 6, pp. 27-32. doi: https://doi.org/10.20998/2074-272X.2023.6.05.
Khelil K., Berrezzek F., Bouadjila T. GA-based design of optimal discrete wavelet filters for efficient wind speed forecasting. Neural Computing and Applications, 2021, vol. 33, no. 9, pp. 4373-4386. doi: https://doi.org/10.1007/s00521-020-05251-5.
Bengharbi A.A., Laribi S., Allaoui T., Mimouni A. Photovoltaic system faults diagnosis using discrete wavelet transform based artificial neural networks. Electrical Engineering & Electromechanics, 2022, no. 6, pp. 42-47. doi: https://doi.org/10.20998/2074-272X.2022.6.07.
Akkouchi K., Rahmani L., Lebied R. New application of artificial neural network-based direct power control for permanent magnet synchronous generator. Electrical Engineering & Electromechanics, 2021, no. 6, pp. 18-24. doi: https://doi.org/10.20998/2074-272X.2021.6.03.
Abid M., Laribi S., Larbi M., Allaoui T. Diagnosis and localization of fault for a neutral point clamped inverter in wind energy conversion system using artificial neural network technique. Electrical Engineering & Electromechanics, 2022, no. 5, pp. 55-59. doi: https://doi.org/10.20998/2074-272X.2022.5.09.
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