Early detection and localization of stator inter-turn short circuit faults based on variational mode decomposition and deep learning in induction motor

• Autorzy: Guedidi Asma , Laala Widad , Guettaf Abderrazak , Arif Ali

Identyfikatory

DOI

10.29354/diag/174001

• Języki publikacji: EN

Abstrakty

EN

The existing diagnostic techniques for detecting inter-turn short circuits (ITSCs) in induction motors face two primary challenges. Firstly, they suffer from reduced sensitivity, often failing to detect ITSCs when only a few turns are short-circuited. Secondly, their reliability are compromised by load fluctuations, leading to false alarms even in the absence of actual faults. To address these issues, a novel intelligent approach to diagnose ITSC fault is proposed. Indeed, this method encompasses three core components: a novel multi-sensor fusion technique, a knowledge map, and enhanced Convolutional Neural Networks (CNNs). First, the raw data collected from multiple sensors undergoes a transformation into 2D data using a novel image transformation based on Hilbert transform (HT) and variational mode decomposition (VMD), which is concatenate to a novel information map including frequency fault information and rotational speed. Then, this 3D multi information image is used as input to an improvement CNN model that apply a transfer learning for an enhanced version of SqueezNet with incorporating a novel attention mechanism module to precisely identify fault features. Experimental results and performance comparisons demonstrate that the proposed model attains high performance surpassing other Deep Learning (DL) methods in terms of accuracy. In addition, the model has consistently demonstrated its ability to make precise predictions and accurately classify fault severity, even under different working conditions.

Słowa kluczowe

EN

convolutional neural networks; CNNs; deep learning; short circuit fault diagnosis; variational mode decomposition; information map

PL

silnik indukcyjny; konwolucyjne sieci neuronowe; uczenie głębokie; mapa informacyjna

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2023
• Tom: Vol. 24, No. 4
• Strony: art. no. 2023401
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Guedidi Asma

• Electrical Engineering Department, The Energy Systems Modeling Laboratory (LMSE) Laboratory, University of Biskra, B.P. 145, 07000, Biskra, Algeria

• https://orcid.org/0000-0003-4826-8527

autor

Laala Widad

• Electrical Engineering Department, The Electrical Engineering Laboratory of Biskra (LGEB), University of Biskra, B.P. 145, 07000, Biskra, Algeria

autor

Guettaf Abderrazak

• Electrical Engineering Department, The Energy Systems Modeling Laboratory (LMSE) Laboratory, University of Biskra, B.P. 145, 07000, Biskra, Algeria

• https://orcid.org/0000-0002-2725-0742

autor

Arif Ali

• Electrical Engineering Department, The Energy Systems Modeling Laboratory (LMSE) Laboratory, University of Biskra, B.P. 145, 07000, Biskra, Algeria

• https://orcid.org/0000-0002-2557-5682

Bibliografia

• 1. Lee H, Jeong H, Koo G, Ban J, Kim SW. Attention recurrent neural network-based severity estimation method for interturn short-circuit fault in permanent magnet synchronous machines. IEEE Transactions on Industrial Electronics 2020; 68(4): 3445-3453. https://doi.org/10.1109/TIE.2020.2978690.
• 2. Gerlici J, Goolak S, Gubarevych O, Kravchenko K, Kamchatna-Stepanova K, Toropov A. Method for determining the degree of damage to the stator windings of an induction electric motor with an asymmetric power system. Symmetry 2022; 14(7): 1305. https://doi.org/10.3390/sym14071305.
• 3. Alloui A, Laadjal K, Sahraoui M, Cardoso AJM. 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 2022; 70(3): 3080-3089. https://doi.org/10.1109/TIE.2022.3172751.
• 4. Gubarevych O, Goolak S, Melkonova I, Yurchenko M. Structural diagram of the built-in diagnostic system for electric drives of vehicles. Diagnostyka 2022; 23(4): 2022406. https://doi.org/10.29354/diag/156382.
• 5. Guangxing N, Enhui L, Xuan W, Paul Z, Bin Z. Enhanced discriminate feature learning deep residual CNN for multitask bearing fault diagnosis with information fusion. IEEE Transactions on Industrial Informatics 2023; 19(1): 762-770. https://doi.org/10.1109/TII.2022.3179011.
• 6. Mejia-Barron A, Tapia-Tinoco G, Razo-Hernandez JR, Valtierra-Rodriguez M, Granados-Lieberman D. A neural network-based model for MCSA of inter-turn short-circuit faults in induction motors and its power hardware in the loop simulation. Computers & Electrical Engineering., 2021; 93: 107234. https://doi.org/10.1016/j.compeleceng.2021.107234.
• 7. Zhang Z, Ma J, Xiangli K, Ma Y, Gong X, Xu J. Diagnosis of Inter-Turn Short Circuit Fault Based on Wavelet Transform and PSO-SVM. 2021 6th International Conference on Transportation Information and Safety (ICTIS) 2021; 1025-1028. https://doi.org/10.1109/ICTIS54573.2021.9798685.
• 8. Çetin M, Sarica Y. Artificial Intelligence Based Game Levelling. Balkan Journal of Electrical and Computer Engineering 2020; 8(2): 147-153 https://doi.org/10.17694/bajece.650484.
• 9. Huang J, Lin R, He Z, Song H, Huang X, Chen B. Application of WOA-VMD-SVM in fault diagnosis of generator inter-turn short circuit. 2022 China Automation Congress (CAC). IEEE, 2022. https://doi.org/10.1109/CAC57257.2022.10055106.
• 10. Bachir S, Tnani S, Trigeassou JC, Champenois G. Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Transactions on Industrial Electronics 2006; 53(3): 963-973. https://doi.org/10.1109/TIE.2006.874258.
• 11. Aubert B, Régnier J, Caux S, Alejo D. Kalman-Fiterbased indicator for online interturn short circuits detection in permanent-magnet synchronous generators. IEEE Transactions on Industrial Electronics 2015;62(3):1921-1930. https://doi.org/10.1109/TIE.2014.2348934.
• 12. Ozgan IH, Devecioglu OC, Ince T, Askar M. Enhanced bearing fault detection using multichannel, multilevel 1D CNN classifier. Electrical Engineering 2022; 104: 435-447. https://doi.org/10.1007/s00202- 021-01309-2.
• 13. He J, Li X, Chen Y, Chen D, Guo J, Zhou Y. Deep transfer learning method based on 1D-CNN for bearing fault diagnosis. Shock and Vibration 2021; 1-16. doi.org/10.1155/2021/6687331.
• 14. Imene M, Nesbitt A, Conner S, Boreham P, Morison G. 1D‐CNN based real‐time fault detection system for power asset diagnostics. IET Generation, Transmission & Distribution 2020; 14(24): 5766-5773. https://doi.org/10.1049/iet-gtd.2020.0773.
• 15. Huang D, Li S, Qin N, Zhang Y. Fault diagnosis of high-speed train bogie based on the improvedCEEMDAN and 1-D CNN algorithms. IEEE Transactions on Instrumentation and Measurement 2021; 70: 1-11. https://doi.org/10.1109/TIM.2020.3047922.
• 16. Jinsong Y, Liu J, Xie J, Wang C, Ding T. Conditional GAN and 2-D CNN for bearing fault diagnosis with small samples. IEEE Transactions on Instrumentation and Measurement 2021; 70: 1-12. https://doi.org/10.1109/TIM.2021.3119135.
• 17. Haiyoung J, Choi S, Lee B. Rotor fault diagnosis method using CNN-Based transfer learning with 2D sound spectrogram analysis. Electronics 2023; 12(3): 480. https://doi.org/10.3390/electronics12030480.
• 18. Pham MT, Kim JM, Kim CH. 2D CNN-based multioutput diagnosis for compound bearing faults under variable rotational speeds. Machines 2021; 9(9): 199. https://doi.org/10.3390/machines9090199.
• 19. Zhong, SS, Fu S, Lin L. A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement 2019; 137: 435-453. https://doi.org/10.1016/j.measurement.2019.01.022.
• 20. Huangfu, H., Zhou, Y., Zhang, J., Ma, S., Fang, Q., & Wang, Y. Research on inter-turn short circuit fault diagnosis of electromechanical actuator based on transfer learning and VGG16. Electronics;2022, 11(8), 1232.https://doi.org/10.3390/electronics11081232.
• 21. Huan S, Li J, Zhang Y, Wang Q. VMD-CNN: Dual feature extraction for detection of turn-to-turn short circuit faults in permanent magnet synchronous motors. Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence 2022; 224-230. https://doi.org/10.1145/3577530.3577566.
• 22. Skowron M, Orłowska-Kowalska T, Wolkiewicz M, Kowalski CT. Convolutional neural network-based stator current data-driven incipient stator fault diagnosis of inverter-fed induction motor. Energies 2020; 13(6), 1475. https://doi.org/10.3390/en13061475.
• 23. Laohu Y Lian D, Kang X, Chen Y, Zhai K. Rolling bearing fault diagnosis based on convolutional neural network and support vector machine. IEEE Access 2020; 8: 137395-137406. https://doi.org/10.1109/ACCESS.2020.3012053.
• 24. Guo S, Yang T, Gao W, Zhang C. A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors 2018; 18(5):1429. https://doi.org/10.3390/s18051429.
• 25. Moradzadeh A, Moayyed H, Mohammadi-Ivatloo B, Gharehpetian GB, Aguiar AP. Turn-to-turn short circuit fault localization in transformer winding via image processing and deep learning method. IEEE Transactions on Industrial Informatics 2021; 18(7): 4417-4426. https://doi.org/10.1109/TII.2021.3105932.
• 26. Liu R, Meng G, Yang B, Sun C, Chen X. Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine. IEEE Transactions on Industrial Informatics 2016; 13(3): 1310-1320. https://doi.org/10.1109/TII.2016.2645238.
• 27. Wang X, Mao D, Li X. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement 2021; 173: 108518. https://doi.org/10.1016/j.measurement.2020.108518.
• 28. Ding X, He Q. Energy-fluctated mulstiscale feature learning with deep convent for intelligent spindle bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement 2017, 66(8), 1926- 1935. https://doi.org/10.1109/TIM.2017.2674738.
• 29. Jiang X, Yang S, Wang F, Shengli X, Wang X, Cheng X. OrbitNet: A new CNN model for automatic fault diagnostics of turbomachines. Applied Soft Computing 2021; 110: 107702 https://doi.org/10.1016/j.asoc.2021.107702.
• 30. [Online]: https://medium.com/codex/understandingconvolutional-neural-networks-a-beginners-journeyinto-architecture-aab30dface10.
• 31. Sun W, Zhao H, Jin Z. A facial expression recognition method based on ensemble of 3D convolutional neural networks. Neural Computing and Applications 2019, 31(7): 2795-2812. https://doi.org/10.1007/s00521- 017-3230-2.
• 32. Fang Y, Wang M, Wei L. deep transfer learning in inter-turn short circuit fault diagnosis of PMSM. 2021 IEEE International Conference on Mechatronics and Automation (ICMA) 2021; 489-494. https://doi.org/10.1109/ICMA52036.2021.9512785.
• 33. Yi C, Lv Y, Zhang D. A fault diagnosis scheme for rolling bearing based on particle swarm optimization in variational mode decomposition. Shock and Vibration, 2016; 2016(2): 1-10. https://doi.org/10.1155/2016/9372691.
• 34. Guedidi A, Guettaf A, Cardoso AJM, Laala W, Arif A. Bearing faults classification based on variational mode decomposition and artificial neural network. 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) 2019. https://doi.org/10.1109/DEMPED.2019.8864830.
• 35. De Angelo CH, Bossio GR, Giaccone SJ, Valla MI, Solsona JA, García GO. Online model-based statorfault detection and identification in induction motors. IEEE Transactions on Industrial Electronics 2009; 56(11): 4671-4680. https://doi.org/10.1109/TIE.2009.2012468.
• 36. Guedidi A, Laala W, Guettaf A, Zouzou SE. Diagnosis and Classification of broken bars fault using DWT and Artificial Neural Network without slip estimation. 2020 XI International Conference on Electrical Power Drive Systems (ICEPDS) 2020; 1-7. IEEE. https://doi.org/10.1109/ICEPDS47235.2020.9249315.
• 37. Peng D, Wang H, Liu Z, Zhang W, Zuo MJ. and Chen. J. (2020). Multibranch and Multiscale CNN for Fault Diagnosis of Wheelset Bearings Under Strong Noise and Variable Load Condition. IEEE Transactions on Industrial Informatics 2020; 16(7): 4949-4960. https://doi.org/10.1109/TII.2020.2967557.
• 38. Kumar P, Kumar P, Hati AS, Kim HS. Deep transfer learning framework for bearing fault detection in motors. Mathematics 2020;10:4683. https://doi.org/10.3390/math10244683.