{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:53:04Z","timestamp":1770749584075,"version":"3.50.0"},"reference-count":41,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ22E050001"],"award-info":[{"award-number":["LZ22E050001"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["ZC304022984"],"award-info":[{"award-number":["ZC304022984"]}]},{"name":"Postdoctoral Fellowship Program of Zhejiang Normal University","award":["LZ22E050001"],"award-info":[{"award-number":["LZ22E050001"]}]},{"name":"Postdoctoral Fellowship Program of Zhejiang Normal University","award":["ZC304022984"],"award-info":[{"award-number":["ZC304022984"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB\/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase \u201ca\u201d of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs.<\/jats:p>","DOI":"10.3390\/s23167109","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T12:10:23Z","timestamp":1691755823000},"page":"7109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition"],"prefix":"10.3390","volume":"23","author":[{"given":"Attiq Ur","family":"Rehman","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"},{"name":"Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source, Zhejiang Normal University, Jinhua 321004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9815-6457","authenticated-orcid":false,"given":"Weidong","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, Jinhua 321004, China"},{"name":"School of Engineering, Zhejiang Normal University, Jinhua 321004, China"}]},{"given":"Jianfeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Operation and Maintenance Technology & Equipment for Urban Rail Transit of Zhejiang Province, Jinhua 321004, China"},{"name":"School of Engineering, Zhejiang Normal University, Jinhua 321004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0218-3595","authenticated-orcid":false,"given":"Muhammad","family":"Sohaib","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China"},{"name":"Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source, Zhejiang Normal University, Jinhua 321004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0890-615X","authenticated-orcid":false,"given":"Yonghua","family":"Jiang","sequence":"additional","affiliation":[{"name":"Xingzhi College, Zhejiang Normal University, Lanxi 321100, China"}]},{"given":"Mahnoor","family":"Shahzadi","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu 610054, China"}]},{"given":"Muhammad Ijaz","family":"Khan","sequence":"additional","affiliation":[{"name":"Institute of Mechanical & Manufacturing Engineering, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","unstructured":"Toliyat, H.A., Nani, S., Choi, S., and Meshgin-Kelk, H. (2013). Electrical Machines Modeling, Condition Monitoring and Fault Diagnosis, Taylor & Francis Group."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/TEC.2020.2982515","article-title":"Influence of pole-pair combinations on the characteristics of the brushless doubly fed induction generator","volume":"35","author":"Oraee","year":"2020","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Rana, K., and Meena, D.C. (2018, January 22\u201324). Self excited induction generator for isolated pico hydro station in remote areas. Proceedings of the 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India.","DOI":"10.1109\/ICPEICES.2018.8897329"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shek, J.K.H., Dorrell, D.G., Hsieh, M., Macpherson, D.E., and Mueller, M.A. (2011, January 6\u20138). Reducing bearing wear in induction generators for wave and tidal current energy devices. Proceedings of the IET Conference on Renewable Power Generation (RPG 2011), Edinburgh, UK.","DOI":"10.1049\/cp.2011.0220"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kumar, R.R., Andriollo, M., Cirrincione, G., Cirrincione, M., and Tortella, A. (2022). A comprehensive review of conventional and intelligence-based approaches for the fault diagnosis and condition monitoring of induction motors. Energies, 15.","DOI":"10.3390\/en15238938"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Halder, S., Bhat, S., Zychma, D., and Sowa, P. (2022). Broken rotor bar fault diagnosis techniques based on motor current signature analysis for induction motor\u2014A review. Energies, 15.","DOI":"10.3390\/en15228569"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sohaib, M., Kim, C.-H., and Kim, J.-M. (2017). A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis. Sensors, 17.","DOI":"10.3390\/s17122876"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.3390\/en7042595","article-title":"Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges","volume":"7","author":"Tchakoua","year":"2014","journal-title":"Energies"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Garcia-Calva, T.A., Morinigo-Sotelo, D., Fernandez-Cavero, V., Garcia-Perez, A., and Romero-Troncoso, R.d.J. (2021). Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients. Energies, 14.","DOI":"10.3390\/en14051469"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., Burriel-Valencia, J., and Riera-Guasp, M. (2020). Fault Diagnosis in the Slip\u2013Frequency Plane of Induction Machines Working in Time-Varying Conditions. Sensors, 20.","DOI":"10.3390\/s20123398"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1109\/TIE.2019.2905821","article-title":"In-service wind turbine DFIG diagnosis using current signature analysis","volume":"67","author":"Artigao","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","first-page":"1257","article-title":"A review of the development of wind turbine generators across the world","volume":"45202","author":"Goudarzi","year":"2012","journal-title":"ASME Int. Mech. Eng. Congr. Expo."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yakhni, M.F., Cauet, S., Sakout, A., Assoum, H., Etien, E., Rambault, L., and El-Gohary, M. (2023). Variable speed induction motors\u2019 fault detection based on transient motor current signatures analysis: A review. Mech. Syst. Signal Process., 184.","DOI":"10.1016\/j.ymssp.2022.109737"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4127","DOI":"10.1109\/TIE.2008.2004665","article-title":"A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems","volume":"55","author":"Grubic","year":"2008","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1080\/15325008.2017.1358777","article-title":"Detection of stator winding inter-turn short circuit fault in induction motor using vibration signals by MEMS accelerometer","volume":"45","author":"Hegde","year":"2017","journal-title":"Electr. Power Compon. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rocha, M., Lucas, G., Souza, W., de Castro, B.A., and Andreoli, A. (2021, January 15\u201318). Detection and phase identification of inter-turn short-circuit faults in three-phase induction motors using MEMS accelerometer and Hilbert transform. Proceedings of the 2021 14th IEEE International Conference on Industry Applications (INDUSCON), Sao Paulo, Brazil.","DOI":"10.1109\/INDUSCON51756.2021.9529634"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Misra, S., Kumar, S., Sayyad, S., Bongale, A., Jadhav, P., Kotecha, K., Abraham, A., and Gabralla, L.A. (2022). Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data. Sensors, 22.","DOI":"10.3390\/s22218210"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, M.-C., Lee, J.-H., Wang, D.-H., and Lee, I.-S. (2023). Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods. Sensors, 23.","DOI":"10.3390\/s23052585"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, X., and Zhou, Y. (2023). Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement. Machines, 11.","DOI":"10.3390\/machines11040475"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deng, C., Deng, Z., Lu, S., He, M., Miao, J., and Peng, Y. (2023). Fault Diagnosis Method for Imbalanced Data Based on Multi-Signal Fusion and Improved Deep Convolution Generative Adversarial Network. Sensors, 23.","DOI":"10.3390\/s23052542"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13149","DOI":"10.1109\/ACCESS.2021.3050876","article-title":"Numerical modeling, electrical characteristics analysis and experimental validation of severe inter-turn short circuit fault conditions on stator winding in DFIG of wind turbines","volume":"9","author":"Chen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1007\/s00202-020-00933-8","article-title":"Fault detection and fault severity calculation for rotor windings based on spectral, wavelet and ratio computation analyses of rotor current signals for a doubly fed induction generator in wind turbines","volume":"102","author":"Rehman","year":"2020","journal-title":"Electr. Eng."},{"key":"ref_23","unstructured":"Rehman, A.U., Chen, Y., Huang, G., Yang, Y., Wang, S., Zhao, Y., Zhao, Y., Cheng, Y., and Tanaka, T. (2020, January 15\u201317). Stator inter-turns short circuit fault detection in DFIG using empirical mode decomposition method on leakage flux. Proceedings of the 2020 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), Xi\u2019an, China."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s00521-010-0512-3","article-title":"Vibration signal analysis for electrical fault detection of induction machine using neural networks","volume":"20","author":"Su","year":"2011","journal-title":"Neural Comput. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.ymssp.2013.08.033","article-title":"Detection of stator winding faults in induction machines using flux and vibration analysis","volume":"42","author":"Pederiva","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Antonino-Daviu, J., Zamudio-Ram\u00edrez, I., Osornio-R\u00edos, R.A., Fuster-Roig, V., de Jes\u00fas Romero-Troncoso, R., and Dunai, L.D. (2019, January 14\u201317). Stray flux analysis for the detection of rotor failures in wound rotor induction motors. Proceedings of the IECON 2019\u201445th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal.","DOI":"10.1109\/IECON.2019.8927619"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1080\/15325008.2018.1562647","article-title":"Monitoring of wound rotor induction machines by means of discrete wavelet transform","volume":"46","author":"Kia","year":"2018","journal-title":"Electr. Power Compon. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sabir, H., Ouassaid, M., and Ngote, N. (2018, January 5\u20138). December. Diagnosis of rotor winding inter-turn short circuit fault in wind turbine based on DFIG using hybrid TSA\/DWT approach. Proceedings of the 2018 6th International Renewable and Sustainable Energy Conference (IRSEC), Rabat, Morocco.","DOI":"10.1109\/IRSEC.2018.8703006"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dahi, K., Elhani, S., and Guedira, S. (November, January 29). Statistical wound-rotor IM diagnosis method based on standard deviation using NVSA. Proceedings of the IECON 2014\u201440th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, USA.","DOI":"10.1109\/IECON.2014.7048618"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3047492","article-title":"A new acoustic emission-based approach for supply disturbances evaluation in three-phase induction motors","volume":"70","author":"Lucas","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Santos, V.V.D., Castro, B.A.D., Binotto, A., Rey, J.A.A., Lucas, G.B., and Andreoli, A.L. (2020). An application of wavelet analysis to assess partial discharge evolution by acoustic emission sensor. Eng. Proc., 2.","DOI":"10.3390\/ecsa-7-08244"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIA.2019.2895797","article-title":"Machine learning-based fault diagnosis for single-and multi-faults in induction motors using measured stator currents and vibration signals","volume":"55","author":"Ali","year":"2019","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"241","DOI":"10.2478\/msr-2019-0031","article-title":"Detection of deterioration of three-phase induction motor using vibration signals","volume":"19","author":"Glowacz","year":"2019","journal-title":"Meas. Sci. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Schiewaldt, K., Lucas, G., Rocha, M., Fraga, C., and Andreoli, A. (2019). Identification of stator winding insulation faults in three-phase induction motors using MEMS accelerometers. Proceedings, 42.","DOI":"10.3390\/ecsa-6-06630"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Krause, P., Wasynczuck, O., and Sudhoff, S. (2002). Analysis of Electric Machinery and Drive Systems, John Wiley & Sons. [2nd ed.].","DOI":"10.1109\/9780470544167"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/TIE.2006.874258","article-title":"Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines","volume":"53","author":"Bachir","year":"2006","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_37","unstructured":"Kashyap, K.H., and Shenoy, U.J. (2003, January 25\u201328). Classification of power system faults using wavelet transforms and probabilistic neural networks. Proceedings of the 2003 International Symposium on Circuits and Systems 2003, ISCAS\u201903, Bangkok, Thailand."},{"key":"ref_38","unstructured":"Gopinath, R.A., Guo, H., Burrus, C.S., and Burrus, L.S. (1997). Introduction to Wavelets and Wavelet Transforms: A Primer, Stanford University."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/61.891505","article-title":"Wavelet based on-line disturbance detection for power quality applications","volume":"15","author":"Karimi","year":"2000","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/61.277693","article-title":"A new neural networks approach to on-line fault section estimation using information of protective relays and circuit breakers","volume":"9","author":"Yang","year":"1994","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.renene.2018.05.043","article-title":"Does renewable energy consumption and health expenditures decrease carbon dioxide emissions? Evidence for sub-Saharan Africa countries","volume":"127","author":"Apergis","year":"2018","journal-title":"Renew. Energy"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7109\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:31:21Z","timestamp":1760128281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/16\/7109"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,11]]},"references-count":41,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23167109"],"URL":"https:\/\/doi.org\/10.3390\/s23167109","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,11]]}}}