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Diagnosis of multiple faults of an induction motor based on Hilbert envelope analysis

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Three phase induction motors are widely used in industrial processes and condition monitoring of these motors is especially important. Broken rotor bars, eccentricity and bearing faults are the most common types of faults of induction motors. Stator current and/or vibration signals are mostly preferred for the monitoring and detection of these faults. Fourier Transform (FT) based detection methods analyse the characteristic harmonic components of stator current and vibration signals for feature extraction. Several types of simultaneous faults of induction motors may produce characteristic harmonic components at the same frequency (with varying amplitudes). Therefore, detection of multiple faults is more difficult than detection of a single fault with FT based diagnosis methods. This paper proposes an alternative approach to detect simultaneous multiple faults including broken rotor bars, static eccentricity and outer/inner-race bearing faults by analysing stator current and vibration signals. The proposed method uses Hilbert envelope analysis with a Normalized Least Mean Square (NLSM) adaptive filter. The results are experimentally verified under 25%, 50%, 75%, 100% load conditions.
Rocznik
Strony
191--205
Opis fizyczny
Bibliogr. 34 poz., rys, tab, wykr., wzory
Twórcy
autor
  • Burdur Mehmet Akif Ersoy University, Department of Electrical and Electronic Engineering, 15030, Burdur, Turkey
  • Kütahya Dumlupınar University, Department of Electrical and Electronic Engineering, 43100, Kütahya, Turkey
Bibliografia
  • [1] Tousizadeh, M., Che, H. S., Selvaraj, J., Rahim, N. A., & Ooi, B.-T. (2019). Fault-tolerant field-oriented control of three-phase induction motor based on unified feedforward method. IEEE Transactions on Power Electronics, 34(8), 7172-7183. https://www.doi.org/10.1109/TPEL.2018.2884759
  • [2] Matic, D. & Kanovic, Z. (2017). Vibration based broken bar detection in induction machine for low load conditions. Advances and Electrical and Computer Engineering, 17(1), 49-54. https://www.doi.org/10.4316/AECE.2017.01007
  • [3] Liang, X., Ali, M. Z., & Zhang, H. (2020). Induction motors fault diagnosis using finite element method: A review. IEEE Transactions on Industry Applications, 56(2), 1205-1217. https://www.doi.org/10.1109/TIA.2019.2958908
  • [4] Jiang, Q., Chang, F. & Sheng, B. (2019). Bearing fault classification based on convolutional neural network in noise environment. IEEE Access, 7, 69795-69807. https://www.doi.org/10.1109/ACCESS.2019.2919126
  • [5] Peeters, C., Guillaume, P. & Helsen, J. (2018). Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy. Renewable Energy, 116, 74-87. https://www.doi.org/10.1016/j.renene.2017.01.056
  • [6] Ünsal, A. (2020). Investigation of parallel misalignment faults of induction motor by using entropy analysis. Journal of Polytechnic, 23(4), 1037-1050. https://www.doi.org/10.2339/politeknik.551490
  • [7] Saucedo-Dorantes, J. J., Delgado-Prieto, M., Ortega-Redondo, J. A., Osornio-Rios, R. A. & Romero-Troncoso, R. d. J. (2016). Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain. Shock and Vibration, 2016, 1-13. https://www.doi.org/10-1155/2016/5467643
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  • [13] Unsal, A. & Kabul, A. (2016). Detection of the broken rotor bars of squirrel-cage induction motors based on normalized least mean square filter and Hilbert envelope analysis. Electrical Engineering, 98(3), 245-256. https://doi.org/10.1007/s00202-016-0366-5
  • [14] Romero-Troncoso, R. d. J. (2016). Multirate signal processing to improve FFT-based analysis for detecting faults in induction motors. IEEE Transactions on Industrial Informatics, 13(3), 1291-1300. https://www.doi.org/10.1109/TII.2016.2603968
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  • [17] Chen, Z., Deng, S., Chen, X., Sanchez, R.-V. & Quin, H. (2017). Deep neural networks-based rolling bearing fault diagnosis. Microelectronics Reliability, 75, 327-333. https://www.doi.org/10.1016/j.microrel.2017.03.006
  • [18] Unal, M., Onat, M., Demetgul, M. & Kucuk, H. (2014). Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement, 58, 187-196. https://www.doi.org/10.1016/j.measurement.2014.08.041
  • [19] Bessous, N., Sbaa, S. & Megherbi, A. C. (2019). Mechanical fault detection in rotating electrical machines using MCSA-FFT and MCSA-DWT techniques. Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol 67(3), 571-582. https://www.doi.org/10.24425/bpasts.2019.129655
  • [20] Pandarakone, S. E., Mizuno Y & Nakamura H. (2019). A comparative study between machine learning algorithm and artificial intelligence neural network in detecting minor bearing fault of induction motors. Energies, 12(11), 2105. https://doi.org/10.3390/en12112105
  • [21] Yoo Y. J. (2019). Fault detection of induction motor using Fast Fourier Transform with feature selection via Principal Component Analysis. International Journal of Precision Engineering and Manufacturing, 20, 1543-1552. https://www.doi.org/10.1007/s12541-019-00176-z
  • [22] Pang, B., Tang, G., Tian, T. & Zhou, C. (2018). Rolling bearing fault diagnosis based on improved HTT transform. Sensors, 18(4), 1203. https://www.doi.org/10.3390/s18041203
  • [23] Mishra, C., Samantaray, A. K. & Chakraborty, G. (2017). Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising. Measurement, 103, 77-86. https://www.doi.org/10.1016/j.measurement.2017.02.033
  • [24] Nana, A., Thammayyabbabu, K. R., Samanta, A. K., Routray, A. & Deb, A. K. (2017). Mobile application to detect induction motor faults. IEEE Embedded Systems Letters, 9(4), 117-120. https://www.doi.org/10.1109/LES.2017.2734798
  • [25] Maruthi, G. S. & Hegde, V. (2016). Application of MEMS accelerometer for detection and diagnosis of multiple faults in the roller element bearings of three phase induction motor. IEEE Sensors Journal, 16(1), 145-152. https://www.doi.org/10.1109/JSEN.2015.2476561
  • [26] Yang, T., Pen, H., Wang, Z. & Chang, C. S. (2016). Feature knowledge based fault detection of induction motors through the analysis of stator current data. IEEE Transactions on Instrumentation and Measurement, 65(3), 549-558. https://www.doi.org/10.1109/TIM.2015.2498978
  • [27] Yu, Z., Cai, Y. & Mo, D. (2020). Comparative study on noise reduction effect of fiber optic hydrophone based on LMS and NLMS algorithm. Sensors, Vol 20(1), 301. https://www.doi.org/10.3390/s20010301
  • [28] Kabul, A. & Ünsal, A. (2021). An alternative approach for the detection of broken rotor bars and bearing faults of induction motor based on vibration signals. Proceedings of the 8th International Conference on Electrical and Electronics Engineering (ICEEE), Turkey, 126-131. https://www.doi.org/10.1109/ICEEE52452.2021.9415920
  • [29] Faiz, J. & Moosavt S. M. M. (2016). Eccentricity limit detection - From induction machines to DFIG - A review. Renewable and Sustainable Energy Reviews, 55, 169-179. https://www.doi.org/10.1016/j.rser.201510.113
  • [30] Kabul, A. & Ünsal, A. (2021). Detection of broken rotor bars of induction motors based on the combination of Hilbert envelope analysis and Shannon entropy, tm - Technisches Messen, 88(1), 45-58. https://www.doi.org/10.1515/teme-2020-0066
  • [31] Boudinar, A. H., Benouzza, N., Bendiabdellah, A. & Khodja, M.-E.-A. (2016). Induction motor bearing fault analysis using a Root-MUSIC method. IEEE Transactions on Industry Applications, 52(5), 3851-3860. https://www.doi.org/10.1109/TIA.2016.2581143
  • [32] GBMN Bearing USA Ltd. (29.04.2021). Data Sheet of 6206 Bearing, https://www.gmnbt.com/catalog/product/bb-6206-radial-ball-bearing/
  • [33] Hujare, D. P. & Karnik, M. G. (2018). Vibration responses of parallel misalignment in Al shaft rotor bearing system with rigid coupling. Materials Today: Proceedings, 5(11), 23863-23871. https://www.doi.org/10.1016/j.matpr.2018.10.178
  • [34] Wang, Z., Yang, J., Li, H., Zhen, D., Xu Y. & Gu, F. (2019). Fault identification of broken rotor bars in induction motors using an improved cyclic modulation spectral analysis. Energies, 12(17), 3279. https://www.doi.org/10.3390/en12173279
Uwagi
1. This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with a grant number 116E302.
2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0a807351-ff92-4729-9b87-923543956607
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