Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

doi:10.23919/CJEE.2023.000003

Chinese Journal of Electrical Engineering ›› 2023, Vol. 9 ›› Issue (1): 142-157.doi: 10.23919/CJEE.2023.000003

• Regular Papers • Previous Articles Next Articles

扫码分享

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Abdelelah Almounajjed^1,*, Ashwin Kumar Sahoo¹, Mani Kant Kumar², Sanjeet Kumar Subudhi¹

1. Electrical Engineering Department, C.V Raman Global University, Bhubaneswar 752054, India;
2. Electronics and Communication Engineering Department, C.V Raman Global University, Bhubaneswar 752054, India

Received:2021-10-19 Revised:2021-12-27 Accepted:2022-01-19 Online:2023-03-25 Published:2023-04-06
Contact: *E-mail: abdelelah_almounajjed@hotmail.com
About author:Abdelelah Almounajjed received the M.E. degree from Al-Baath University, Homs, Syria, in 2017. He is currently a Ph.D. student in C. V. Raman Global University, Odisha. India. He worked as a lecturer for five years at Al-Baath university. His current research interests are in machine fault diagnosis, signal processing and artificial intelligence applications.
Ashwin Kumar Sahoo , Professor & HOD in the Department of Electrical Engineering, C. V. Raman Global University, Bhubaneswar, Odisha. He has 25 years of teaching & research experience and 2 years of industrial experience. His research interest includes FACTS, microgrid and fault analysis.
Mani Kant Kumar received the M.E. degree from Thapar University, Patiala, India, in 2010 and the Ph.D. degree from Motilal Nehru National Institute of Technology Allahabad (MNNIT Allahabad), Pragayraj, India, in 2019. He is currently working as an assistant professor in the Department of Electronics and Communication Engineering, C. V. Raman Global University, Bhubaneswar 752054, India. His current research interests are in signal processing and nonlinear dynamical systems.
Sanjeet Kumar Subudhi received his B. Tech degree from Silicon Institute of Technology, Biju Pattanaik University of Technology, Odisha, India, in 2008, and M. Tech. degree in Electrical Engineering from Indian Institute of Technology Kharagpur, West Bengal, India, in 2014. Since 2014, he has been working toward the Ph.D. degree in Electrical Engineering Department, National Institute of Technology Rourkela, Odisha, India. Presently, he is working as an assistant professor in Electrical Engineering Department, C. V. Raman Global University, Odisha, India. His research interests include nonlinear dynamics, microgrids, control of switched mode power converters, and power systems.

Abstract

Abstract: A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding. Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters L₁ and L₂ norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining L₁ and L₂ norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

Key words: Discrete wavelet transform, induction motor, inter-turn short circuit fault, neural networks, statistical parameters

Abdelelah Almounajjed, Ashwin Kumar Sahoo, Mani Kant Kumar, Sanjeet Kumar Subudhi. Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique[J]. Chinese Journal of Electrical Engineering, 2023, 9(1): 142-157.

References

[1] A Almounajjed, A K Sahoo, M K Kumar, et al.Condition monitoring and fault diagnosis of induction motor: An experimental analysis.In 2021 7th Int. Conf. on Electrical Energy Systems (ICEES), 2021: 433-438.
[2] A Almounajjed, A K Sahoo, M K Kumar, et al.Investigation techniques for rolling bearing fault diagnosis using machine learning algorithms.In 2021 5th Int. Conf. on Intelligent Computing and Control Systems (ICICCS), 2021: 1290-1294.
[3] A Almounajjed, A K Sahoo, M K Kumar, et al.Fault diagnosis and investigation techniques for induction motor.International Journal of Ambient Energy, 2022, 43(1): 6341-6361.
[4] A Almounajjed, A K Sahoo, M K Kumar.Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning.Electrical Engineering, 2022, 104: 2859-2877.
[5] C Y Lee, M S Wen.Establish induction motor fault diagnosis system based on feature selection approaches with MRA.Processes, 2020, 8(9): 1-55.
[6] A Almounajjed, A K Sahoo.Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor.Transactions of the Institute of Measurement and Control, 2023, 45(2): 261-273.
[7] F Aghazadeh, A Tahan, M Thomas.Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process.The International Journal of Advanced Manufacturing Technology, 2018, 98(9): 3217-3227.
[8] I Zamudio-Ramirez, R A Osornio-Rios, R de J Romero- Troncoso, et al. Wavelet entropy to estimate the winding insulation healthiness in induction motors. IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, October 14-17, 2019, Lisbon, Portugal. IEEE, 2019, 1: 3716-3722.
[9] H Khelfi, S Hamdani.Induction motor rotor fault diagnosis using three-phase current intersection signal.Electrical Engineering, 2020, 102(2): 539-548.
[10] Y Wang, L Yang, J Xiang, et al.A hybrid approach to fault diagnosis of roller bearings under variable speed conditions.Measurement Science and Technology, 2017, 28(12): 125104.
[11] G H Bazan, P R Scalassara, W Endo, et al.Stator short-circuit diagnosis in induction motors using mutual information and intelligent systems.IEEE Transactions on Industrial Electronics, 2018, 66(4): 3237-3246.
[12] R J Romero-Troncoso, R Saucedo-Gallaga, E Cabal-Yepez, et al. FPGA based online detection of multiple combined faults in induction motors through information entropy and fuzzy inference.IEEE Transactions on Industrial Electronics, 2011, 58(11): 5263-5270.
[13] Y Yang, X Dong, Z Peng, et al.Vibration signal analysis using parameterized time-frequency method for features extraction of varying-speed rotary machinery.Journal of Sound and Vibration, 2015, 335: 350-366.
[14] J A Antonino-Daviu, M Riera-Guasp, J R Folch, et al. Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines.IEEE Transactions on Industry Applications, 2006, 42(4): 990-996.
[15] Y M Hsueh, V R Ittangihal, W B Wu, et al.Fault diagnosis system for induction motors by CNN using empirical wavelet transform.Symmetry, 2019, 11(10): 12-21.
[16] R N Dash, B Subudhi, S Das.Induction motor stator inter-turn fault detection using wavelet transform technique.IEEE 2010 5th International Conference on Industrial and Information Systems, 2010: 436-441.
[17] A Jawadekar, S Paraskar, S Jadhav, et al.Artificial neural network-based induction motor fault classifier using continuous wavelet transform.Systems Science and Control Engineering, 2014, 2(1): 684-690.
[18] B A Vinayak, K A Anand, G Jagadanand.Wavelet-based real-time stator fault detection of inverter-fed induction motor.IET Electric Power Applications, 2020, 14(1): 82-90.
[19] R H C Palácios, I N da Silva, A Goedtel, et al. Diagnosis of stator faults severity in induction motors using two intelligent approaches.IEEE Transactions on Industrial Informatics, 2017, 13(4): 1681-1691.
[20] W F Godoy, I N da Silva, A Goedtel, et al. Evaluation of stator winding faults severity in inverter-fed induction motors.Applied Soft Computing, 2015, 32: 420-431.
[21] H Cherif, A Benakcha, I Laib, et al.Early detection and localization of stator interturn faults based on discrete wavelet energy ratio and neural networks in induction motor.Energy, 2020, 212: 118684.
[22] B Bessam, A Menacer, M Boumehraz, et al.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, 8(1): 478-488.
[23] G H Bazan, P R Scalassara, W Endo, et al.Stator fault analysis of three-phase induction motors using information measures and artificial neural networks.Electric Power Systems Research, 2017, 143: 347-356.
[24] G Singh, T C A Kumar, V Naikan. Induction motor inter turn fault detection using infrared thermographic analysis.Infrared Physics and Technology, 2016, 77: 277-282.
[25] A Almounajjed, A K Sahoo, M K Kumar.Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis.Measurement, 2021, 182: 109780.
[26] M Sahraoui, A Ghoggal, S E Zouzou, et al.Modeling and detection of inter-turn short circuits in stator windings of induction motor.IECON 2006-32nd Annual Conference on IEEE Industrial Electronics, IEEE, 2006: 4981-4986.
[27] S Bachir, S Tnani, J C Trigeassou, et al.Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines.IEEE Transactions on Industrial Electronics, 2006, 53(3): 963-973.
[28] S M Hosseini, F Hosseini, M Abedi.Stator fault diagnosis of a BLDC motor based on discretewavelet analysis using ADAMS simulation.SN Applied Sciences, 2019, 1(11): 1-13.
[29] B M Ebrahimi, J Faiz, S Lotfi-Fard, et al.Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform.Mechanical Systems and Signal Processing, 2012, 30: 131-145.
[30] A Bouzida, O Touhami, R Ibtiouen, et al.Fault diagnosis in industrial induction machines through discrete wavelet transform.IEEE Transactions on Industrial Electronics, 2010, 58(9): 4385-4395.

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Zhenyu Zhao, Fei Fan, Quqin Sun, Huamin Jie, Zhou Shu, Wensong Wang, Kye Yak See. Physics Informed Neural Network-based High-frequency Modeling of Induction Motors [J]. Chinese Journal of Electrical Engineering, 2022, 8(4): 30-38.
[2]	YIN Shaobo. Study on the Restarting Method of Induction Motor under Speed-sensorless Control [J]. Journal of Electrical Engineering, 2022, 17(3): 130-137.
[3]	DANG Yuemao, ZHANG Xuechun, XU Chuyi, JIANG Quanyuan. Lithium-ion Battery State of Health Assessment Algorithm Based on DT Curve [J]. Journal of Electrical Engineering, 2022, 17(3): 58-65.
[4]	YU Xiaoxia, DENG Lei, TANG Baoping, XIA Yi, LI Qikang. Gear Degradation Trend Prediction by Meta-learning Gated Recurrent Unit Networks under Few Samples [J]. Journal of Mechanical Engineering, 2022, 58(3): 149-156.
[5]	ZHANG Li, CHEN Jiangtao, XIONG Fenfen, REN Chengkun, LI Chao. Meta-Learning Based Multi-Fidelity Deep Neural Networks Metamodel Method [J]. Journal of Mechanical Engineering, 2022, 58(1): 190-200.
[6]	MI Junjie, YAO Jianyong, DENG Wenxiang. Neural Network Based RISE Control of Winding Tension [J]. Journal of Mechanical Engineering, 2021, 57(24): 74-82.
[7]	SHAO Yiping, TAN Jian, LU Jiansha. A Spatio-temporal Fully Convolutional Recurrent Neural Network Based Surface Topography Prediction [J]. Journal of Mechanical Engineering, 2021, 57(20): 292-304.
[8]	Giribabu Dyanamina, Sanjay Kumar Kakodia. Adaptive Neuro Fuzzy Inference System Based Decoupled Control for Neutral Point Clamped Multi Level Inverter Fed Induction Motor Drive [J]. Chinese Journal of Electrical Engineering, 2021, 7(2): 70-82.
[9]	Yongchang Zhang, Xing Wang, Haitao Yang, Boyue Zhang. Robust Predictive Current Control of Induction Motors Based on Linear Extended State Observer [J]. Chinese Journal of Electrical Engineering, 2021, 7(1): 94-105.
[10]	Al-wesabi Ibrahim, M.B. Shafik, Min Ding, Mohammad Abu Sarhan, Zhijian Fang, Ahmed. G. Alareqi, Tariq Al'moqri, Ayman. M. Al-Rassas. PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions^* [J]. Chinese Journal of Electrical Engineering, 2020, 6(4): 106-121.
[11]	KONG Xiangxi, NIU Junkai, LUO Yuanqing, XU Qi. Sommerfeld Effect and Unbalanced Response Analysis in Single Disk Rotor System with Eccentric Mass Considering Induction Motor Model [J]. Journal of Mechanical Engineering, 2020, 56(17): 137-144.
[12]	YI Xiangtong,ZHAO Feng,CHENG Huijie,SHUAI Zhikang,SHEN Zheng. Study on Transient Stability of Islanded Microgrid with Induction Motor Load [J]. Journal of Electrical Engineering, 2020, 15(1): 16-22.
[13]	HU Niaoqing, CHEN Huipeng, CHENG Zhe, ZHANG Lun, ZHANG Yu. Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks [J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18.
[14]	HAN Rui,TANG Youyi,XU Xiongming,JIAO Kaikai,XIN Zhicheng,YAN Zhiwei,BAO Xiaohua. Analysis and Simulation of Electromagnetic Noise of Induction Motors for Electric Vehicles [J]. Journal of Electrical Engineering, 2019, 14(3): 74-80.
[15]	LI Bin, ZHANG Yun, WANG Liping, LI Xuekun. Modeling for CNC Machine Tool Thermal Error Based on Genetic Algorithm Optimization Wavelet Neural Networks [J]. Journal of Mechanical Engineering, 2019, 55(21): 215-220.