{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:53:40Z","timestamp":1774626820483,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"S&amp;T Program of Hebei, China","award":["20312203D"],"award-info":[{"award-number":["20312203D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The early detection of an inter-turn short circuit (ITSC) fault is extremely critical for permanent magnet synchronous motors (PMSMs) because it can lead to catastrophic consequences. In this study, a model-based transfer learning method is developed for ITSC fault detection. The contribution can be summarized as two points. First of all, a Bayesian-optimized residual dilated CNN model was proposed for the pre-training of the method. The dilated convolution is utilized to extend the receptive domain of the model, the residual architecture is employed to surmount the degradation problems, and the Bayesian optimization method is launched to address the hyperparameters tuning issues. Secondly, a transfer learning framework and strategy are presented to settle the new target domain datasets after the pre-training of the proposed model. Furthermore, motor fault experiments are carried out to validate the effectiveness of the proposed method. Comparison with seven other methods indicates the performance and advantage of the proposed method.<\/jats:p>","DOI":"10.3390\/s23229145","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T08:44:27Z","timestamp":1699865067000},"page":"9145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM"],"prefix":"10.3390","volume":"23","author":[{"given":"Mingsheng","family":"Wang","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7733-264X","authenticated-orcid":false,"given":"Qiang","family":"Song","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wuxuan","family":"Lai","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology (BIT), Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5931","DOI":"10.1109\/TPEL.2015.2496142","article-title":"Study of IPMSM Interturn Faults Part I: Development and Analysis of Models with Series and Parallel Winding Connections","volume":"31","author":"Gu","year":"2016","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7214","DOI":"10.1109\/TPEL.2015.2506640","article-title":"Study of IPMSM Interturn Faults Part II: Online Fault Parameter Estimation","volume":"31","author":"Gu","year":"2016","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7260","DOI":"10.1109\/TIE.2018.2879281","article-title":"Severity Estimation of Interturn Short Circuit Fault for PMSM","volume":"66","author":"Qi","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13665","DOI":"10.1109\/TIE.2022.3146557","article-title":"Detection of permanent magnet damage of PMSM drive based on direct analysis of the stator phase currents using convolutional neural network","volume":"69","author":"Skowron","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TMAG.2022.3169173","article-title":"Machine Learning for Inter-turn Short-circuit Fault Diagnosis in Permanent Magnet Synchronous Motors","volume":"58","author":"Shih","year":"2022","journal-title":"IEEE Trans. Magn."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.matcom.2022.01.019","article-title":"A comparative study for stator winding inter-turn short-circuit fault detection based on harmonic analysis of induction machine signatures","volume":"196","author":"Zorig","year":"2022","journal-title":"Math. Comput. Simul."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ullah, Z., and Hur, J. (2018). A comprehensive review of winding short circuit fault and irreversible demagnetization fault detection in PM type machines. Energies, 11.","DOI":"10.3390\/en11123309"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TITS.2020.2987637","article-title":"Comprehensive Evaluation of Inter-Turn Short Circuit Faults in PMSM Used for Electric Vehicles","volume":"22","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3286","DOI":"10.1109\/TII.2021.3054674","article-title":"Intelligent Data-Driven Diagnosis of Incipient Interturn Short Circuit Fault in Field Winding of Salient Pole Synchronous Generators","volume":"18","author":"Ehya","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TIA.2021.3072881","article-title":"Pattern Recognition of Interturn Short Circuit Fault in a Synchronous Generator Using Magnetic Flux","volume":"57","author":"Ehya","year":"2021","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"7505","DOI":"10.1109\/TIE.2020.3009563","article-title":"Detection and Discrimination of Incipient Stator Faults for Inverter-Fed Permanent Magnet Synchronous Machines","volume":"68","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"An, Y., Zhang, K., Liu, Q., Chai, Y., and Huang, X. (2021, January 17\u201318). Deep Transfer Learning Network for Fault Diagnosis under Variable Working Conditions. Proceedings of the 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS), Chengdu, China.","DOI":"10.1109\/SAFEPROCESS52771.2021.9693606"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s11063-021-10719-z","article-title":"Deep Transfer Learning in Mechanical Intelligent Fault Diagnosis: Application and Challenge","volume":"54","author":"Qian","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108487","DOI":"10.1016\/j.ymssp.2021.108487","article-title":"A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges","volume":"167","author":"Li","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6099818","DOI":"10.1155\/2021\/6099818","article-title":"Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning","volume":"2021","author":"Xiang","year":"2021","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"35768","DOI":"10.1109\/ACCESS.2022.3151240","article-title":"Bearing Fault Diagnosis under Small Data Set Condition: A Bayesian Network Method with Transfer Learning for Parameter Estimation","volume":"10","author":"Hou","year":"2022","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Borgli, R.J., Stensland, H.K., Riegler, M.A., and Halvorsen, P. (2019, January 8\u201310). Automatic Hyperparameter Optimization for Transfer Learning on Medical Image Datasets Using Bayesian Optimization. Proceedings of the 2019 13th International Symposium on Medical Information and Communication Technology (ISMICT), Oslo, Norway.","DOI":"10.1109\/ISMICT.2019.8743779"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107374","DOI":"10.1016\/j.knosys.2021.107374","article-title":"Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data","volume":"230","author":"An","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.eswa.2018.01.056","article-title":"A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval","volume":"100","author":"Khatami","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14347","DOI":"10.1109\/ACCESS.2017.2720965","article-title":"Transfer Learning with Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.isatra.2019.03.017","article-title":"Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions","volume":"93","author":"Han","year":"2019","journal-title":"ISA Trans."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105950","DOI":"10.1016\/j.asoc.2019.105950","article-title":"Fault diagnostics between different type of components: A transfer learning approach","volume":"86","author":"Li","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111690","DOI":"10.1016\/j.nucengdes.2022.111690","article-title":"Transfer learning with limited labeled data for fault diagnosis in nuclear power plants","volume":"390","author":"Li","year":"2022","journal-title":"Nucl. Eng. Des."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111536","DOI":"10.1016\/j.measurement.2022.111536","article-title":"A class alignment method based on graph convolution neural network for bearing fault diagnosis in presence of missing data and changing working conditions","volume":"199","author":"Kavianpour","year":"2022","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115368","DOI":"10.1109\/ACCESS.2019.2936243","article-title":"Improved Deep Transfer Auto-Encoder for Fault Diagnosis of Gearbox under Variable Working Conditions with Small Training Samples","volume":"7","author":"He","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9463","DOI":"10.1109\/TIE.2022.3212415","article-title":"Deep Targeted Transfer Learning Along Designable Adaptation Trajectory for Fault Diagnosis Across Different Machines","volume":"70","author":"Yang","year":"2023","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109359","DOI":"10.1016\/j.measurement.2021.109359","article-title":"A novel deep multi-source domain adaptation framework for bearing fault diagnosis based on feature-level and task-specific distribution alignment","volume":"178","author":"Rezaeianjouybari","year":"2021","journal-title":"Measurement"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Song, Q., Wang, M., Lai, W., and Zhao, S. (2022). Multiscale Kernel-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM. Sensors, 22.","DOI":"10.3390\/s22186870"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1109\/TIA.2019.2961878","article-title":"Analysis of Inter-Turn-Short Fault in an FSCW IPM Type Brushless Motor Considering Effect of Control Drive","volume":"56","author":"Ullah","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6731","DOI":"10.1109\/TPEL.2015.2388493","article-title":"Online Interturn Fault Diagnosis of Permanent Magnet Synchronous Machine Using Zero-Sequence Components","volume":"30","author":"Hang","year":"2015","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TIM.2019.2902003","article-title":"Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks","volume":"69","author":"Xu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fang, Y., Wang, M., and Wei, L. (2021, January 8\u201311). Deep Transfer Learning in Inter-turn Short Circuit Fault Diagnosis of PMSM. Proceedings of the 2021 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan.","DOI":"10.1109\/ICMA52036.2021.9512785"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, X., and Gao, L. (2019, January 6\u20138). A New Transfer Learning Based on VGG-19 Network for Fault Diagnosis. Proceedings of the 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Porto, Portugal.","DOI":"10.1109\/CSCWD.2019.8791884"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1109\/TPEL.2022.3207181","article-title":"On Bayesian Optimization-Based Residual CNN for Estimation of Inter-Turn Short Circuit Fault in PMSM","volume":"38","author":"Song","year":"2023","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_35","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neucom.2018.02.111","article-title":"MCFF-CNN: Multiscale comprehensive feature fusion convolutional neural network for vehicle color recognition based on residual learning","volume":"395","author":"Fu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1109\/TII.2019.2941868","article-title":"Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis under Nonstationary Conditions","volume":"16","author":"Liu","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_38","unstructured":"Gelbart, M.A., Snoek, J., and Adams, R.P. (2014). Bayesian Optimization with Unknown Constraints. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s00466-021-02112-3","article-title":"A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features","volume":"69","author":"Krokos","year":"2022","journal-title":"Comput. Mech."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Han, S., Eom, H., Kim, J., and Park, C. (2020, January 15\u201319). Optimal DNN architecture search using Bayesian Optimization Hyperband for arrhythmia detection. Proceedings of the 2020 IEEE Wireless Power Transfer Conference (WPTC), Seoul, Republic of Korea.","DOI":"10.1109\/WPTC48563.2020.9295590"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9145\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:22:10Z","timestamp":1760131330000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/22\/9145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":40,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23229145"],"URL":"https:\/\/doi.org\/10.3390\/s23229145","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]}}}