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Tytuł artykułu

Discrimination of faults in induction machine based on pattern recognition and neural networks techniques

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Dyskryminacja błędów w maszynie indukcyjnej na podstawie rozpoznawania wzorców i technik sieci neuronowych
Języki publikacji
EN
Abstrakty
EN
The work presented in this paper is a contribution in the theme of monitoring and diagnosing of faults in the three-phase squirrel cage induction machine. The proposed approach is based on the pattern recognition methods and the artificial intelligence techniques. For so doing, measurements of the stator currents are carried out on a machine subject to various faults such as: short-circuit in the stator windings, bar breakage, bearing failure and eccentricity fault. These acquisitions are classified in databases in order to process them and calculate their Power Spectral Density (PSD). Then, another database is formed of the digital data of the PSD images of the currents associated with the type of fault. After that, a process of learning and classification by artificial neural networks was developed. The test results show the efficiency, robustness and correctness of the proposed approach for the discrimination of faults of electrical or mechanical origin affecting the machine.
PL
Praca przedstawiona w tym artykule stanowi wkład w temat monitorowania i diagnozowania uszkodzeń w trójfazowej maszynie indukcyjnej klatkowej. Proponowane podejście opiera się na metodach rozpoznawania wzorców i technikach sztucznej inteligencji. W tym celu pomiary prądów stojana są przeprowadzane na maszynie podlegającej różnym usterkom, takim jak: zwarcie w uzwojeniach stojana, pęknięcie pręta, uszkodzenie łożyska i błąd mimośrodowości. Przejęcia te są klasyfikowane w bazach danych w celu ich przetworzenia i obliczenia ich gęstości widmowej mocy (PSD). Następnie tworzona jest kolejna baza danych z cyfrowymi danymi obrazów PSD prądów związanych z rodzajem uszkodzenia. Następnie opracowano proces uczenia się i klasyfikacji przez sztuczne sieci neuronowe. Wyniki testu pokazują skuteczność, niezawodność i poprawność proponowanego podejścia do rozróżnienia wad pochodzenia elektrycznego lub mechanicznego mających wpływ na maszynę.
Rocznik
Strony
55--61
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Laboratory Diagnostic Group, LDEE, University of Sciences and Technology of Oran, - Mohamed Boudiaf - Faculty of Electrical Engineering BP 1505 Al- Mnaouar, 31000 Oran, Algeria
  • Laboratory Diagnostic Group, LDEE, University of Sciences and Technology of Oran, -Mohamed Boudiaf - Faculty of Electrical Engineering BP 1505 Al Mnaouar, 31000 Oran, Algeria
  • Electrical Engineering Department, Yahiya Fares University, Medea, Algeria
Bibliografia
  • [1] M. B. Abdelkader LAKROUT Noureddine HENINI, “Detection of Inter Turn Short Circuit Fault in Induction Motor Using Artificial Neural Network Approach,” presented at the Conference on Electrical Engineering (CEE 2019), 2019.
  • [2] S. Bazine and J. C. Trigeassou, “Les défauts des machines électriques et leur diagnostic,” 2010.
  • [3] D. M. Sonje, P. Kundu, and A. Chowdhury, “Detection and discrimination of simultaneous stator inter-turn and broken rotor bar faults in induction motor,” presented at the 2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), 2015, pp. 133–138.
  • [4] A. Lakrout, M. Bendjebbar, and N. Henini, “Electric Stator Faults Detection in Induction Motor based on Fuzzy Logic Technique,” presented at the 2018 International Conference on Applied Smart Systems (ICASS), 2018, pp. 1–5.
  • [5] P. A. Panagiotou, I. Arvanitakis, N. Lophitis, J. A. Antonino- Daviu, and K. N. Gyftakis, “A new approach for broken rotor bar detection in induction motors using frequency extraction in stray flux signals,” IEEE Transactions on Industry Applications, vol. 55, no. 4, pp. 3501–3511, 2019.
  • [6] A. Boum, N. Y. J. Maurice, L. N. Nneme, and L. M. Mbumda, “Fault diagnosis of an induction motor based on fuzzy logic, artificial neural network and hybrid system,” Int J Control, vol. 8, no. 2, pp. 42–51, 2018.
  • [7] S. S. Refaat, H. Abu-Rub, and A. Iqbal, “ANN-based system for a discrimination between unbalanced supply voltage and phase loss in induction motors,” 2014.
  • [8] S. S. Refaat and H. Abu-Rub, “ANN-based diagnosis of incipient stator winding turn faults for three-phase induction motors in the presence of unbalanced supply voltage,” presented at the IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society, 2015, pp. 005328–005334.
  • [9] M. Harir and A. Bendiabdellah, “Stator current spectral content of an induction motor taking into account saturation effect,” Przegląd Elektrotechniczny, vol. 94, 2018.
  • [10] N. Lashkari and J. Poshtan, “Detection and discrimination of stator interturn fault and unbalanced supply voltage fault in induction motor using neural network,” presented at the The 6th Power Electronics, Drive Systems & Technologies Conference (PEDSTC2015), 2015, pp. 275–280.
  • [11] L. Maraaba, Z. Al-Hamouz, and M. Abido, “An efficient stator inter-turn fault diagnosis tool for induction motors,” Energies, vol. 11, no. 3, p. 653, 2018.
  • [12] F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data,” Mechanical Systems and Signal Processing, vol. 72, pp. 303– 315, 2016.
  • [13] M. E. Iglesias-Martínez, P. F. de Córdoba, J. A. Antonino- Daviu, and J. A. Conejero, “Detection of bar breakages in induction motor via spectral subtraction of stray flux signals,” presented at the 2018 XIII International Conference on Electrical Machines (ICEM), 2018, pp. 1796–1802.
  • [14] M. O. Mustafa, D. Varagnolo, G. Nikolakopoulos, and T. Gustafsson, “Detecting broken rotor bars in induction motors with model-based support vector classifiers,” Control Engineering Practice, vol. 52, pp. 15–23, 2016.
  • [15] A. S. Al-Mashakbeh, D. Mamchur, A. Kalinov, and M. Zagirnyak, “A diagnostic of induction motors supplied using frequency converter basing on current and power signal analysis,” Przegląd Elektrotechniczny, vol. 92, no. 12, pp. 5–8, 2016.
  • [16] M.-N. Saadi, M. Boukhenaf, A. Redjati, and N. Guersi, “Bearing failures detection in induction motors using the stator current analysis based on Hilbert Huang transform,” presented at the 2017 European Conference on Electrical Engineering and Computer Science (EECS), 2017, pp. 101–106.
  • [17] M. B. KOURA and A. H. BOUDINAR, “Comparaison entre la Technique Vibratoire et la Technique des Courants Statoriques: Application au Diagnostic des Roulements a Billes,” presented at the 2018 International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2018, pp. 1–6.
  • [18] H. Shao, H. Jiang, H. Zhao, and F. Wang, “A novel deep autoencoder feature learning method for rotating machinery fault diagnosis,” Mechanical Systems and Signal Processing, vol. 95, pp. 187–204, 2017.
  • [19] I. Ishkova and O. Vítek, “Detection and classification of faults in induction motor by means of motor current signature analysis and stray flux monitoring,” Przegląd Elektrotechniczny, vol. 92, no. 4, pp. 166–170, 2016.
  • [20] P. S. Puhan and S. Behera, “Application of soft computing methods to detect fault in AC motor,” presented at the 2017 International Conference on Advances in Computing, Communication and Control (ICAC3), 2017, pp. 1–5.
  • [21] J. Sun, C. Yan, and J. Wen, “Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning,” IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 1, pp. 185–195, 2017.
  • [22] M. He and D. He, “Deep learning based approach for bearing fault diagnosis,” IEEE Transactions on Industry Applications, vol. 53, no. 3, pp. 3057–3065, 2017.
  • [23] A. Khiam, M. Ouassaid, and N. Ngote, “A Hybrid TSA-Neural Network Approach for Induction Motor Faults Diagnosis,” presented at the 2017 International Renewable and Sustainable Energy Conference (IRSEC), 2017, pp. 1–6.
  • [24] B. Wangngon, N. Sittisrijan, and S. Ruangsinchaiwanich, “Fault detection technique for identifying broken rotor bars by artificial neural network method,” presented at the 2014 17th International Conference on Electrical Machines and Systems (ICEMS), 2014, pp. 3436–3440.
  • [25] V. Sreeram and M. Supriya, “Fault current discrimination during induction motor starting,” presented at the 2016 IEEE 6th International Conference on Power Systems (ICPS), 2016, pp. 1–4.
  • [26] M. Subha, N. S. Kumar, and K. K. Veni, “Artificial Intelligence Based Stator Winding Fault Estimation in Three Phase Induction Motor,” presented at the 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 1929–1933.
  • [27] M. WOLKIEWICZ and G. TARCHAŁA, “Diagnostyka uszkodzeń elektrycznych silnika indukcyjnego zasilanego z przemiennika częstotliwości w zamkniętej strukturze sterowania,” Przegląd Elektrotechniczny, vol. 94, 2018.
  • [28] J. Bulla, A. D. Orjuela-Canón, and O. D. Flórez, “Feature extraction analysis using filter banks for faults classification in induction motors,” presented at the 2018 International Joint Conference on Neural Networks (IJCNN), 2018, pp. 1–6.
  • [29] R. A. Patel and B. Bhalja, “Induction motor rotor fault detection using artificial neural network,” presented at the 2015 International Conference on Energy Systems and Applications, 2015, pp. 45–50.
  • [30] R. Chehda, N. Benouzza, and N. Kada-Belghitri, “Stator current spectrum analysis applied on short-circuit fault diagnosis of SRM,” Przegląd Elektrotechniczny, vol. 95, 2019.
  • [31] N. R. Devi, D. S. Sarma, and P. R. Rao, “Detection and identification of stator inter-turn faults in three-phase induction motor in presence of supply unbalance condition,” presented at the 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), 2014, pp. 1–4.
  • [32] K. URBAŃSKI and D. MAJCHRZAK, “Faults detection in PMSM drive using Artificial Neural Network,” Przegląd Elektrotechniczny, vol. 93, 2017.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ee1f84be-52b0-4e14-945d-ea8348412e84
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