{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:24:31Z","timestamp":1760235871157,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,2]],"date-time":"2021-10-02T00:00:00Z","timestamp":1633132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51867006","51867007"],"award-info":[{"award-number":["51867006","51867007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005329","name":"Natural Science Foundation of Guizhou Province","doi-asserted-by":"publisher","award":["[2018]5781","[2018]1029"],"award-info":[{"award-number":["[2018]5781","[2018]1029"]}],"id":[{"id":"10.13039\/501100005329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>At this stage, the fault diagnosis of the embedded permanent magnet synchronous motor (IPMSM) mostly relies on the analysis of related signals when the motor is running. It requires designers to deeply understand the motor drive system and fault characteristic signals, which leads to a high threshold for fault diagnosis. This study proposes an IPMSM fault diagnosis method based on a multi-level feature fusion spatial pyramid pooling (SPP) network, which can directly diagnose motor faults through motor operating current data. This method uses the finite element software Altair Flux to build symmetrical normal motor and demagnetization faulty motor models, as well as an asymmetrical eccentric fault model; conduct a joint simulation with MATLAB-Simulink to obtain fault current data; convert the collected current data into grayscale images, using the data set expansion method to form training and test data sets; and improve the convolutional neural network (CNN) network structure, that is, adding jump connections after each pooling layer and adding a spatial pyramid pooling layer after the last pooling layer to form a new CNN structure. Experimental results show that the new CNN can extract different levels and different scales of motor fault features hidden in the image, and can effectively diagnose different types of IPMSM faults. Compared with the traditional CNN, the new CNN has a higher fault diagnosis accuracy, up to 98.16%, 2.3% higher.<\/jats:p>","DOI":"10.3390\/sym13101844","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T01:59:47Z","timestamp":1633917587000},"page":"1844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research on Fault Diagnosis of IPMSM for Electric Vehicles Based on Multi-Level Feature Fusion SPP Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Bohai","family":"Liu","sequence":"first","affiliation":[{"name":"Electrical Engineering College, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinmu","family":"Wu","sequence":"additional","affiliation":[{"name":"Electrical Engineering College, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Li","sequence":"additional","affiliation":[{"name":"Electrical Engineering College, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6064-4508","authenticated-orcid":false,"given":"Xiangping","family":"Chen","sequence":"additional","affiliation":[{"name":"Electrical Engineering College, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,2]]},"reference":[{"key":"ref_1","first-page":"395","article-title":"Overview of AC Motor Stator Insulation Fault Diagnosis and Online Monitoring Technology","volume":"39","author":"Dayong","year":"2019","journal-title":"Proc. Chin. Soc. Electr. Eng."},{"key":"ref_2","first-page":"128","article-title":"The current solution method of IPMSM loss minimization for electric vehicles","volume":"44","author":"Wu","year":"2016","journal-title":"J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7671","DOI":"10.1109\/TIE.2016.2590993","article-title":"A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles","volume":"63","author":"Kommuri","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.23919\/CJEE.2016.7933115","article-title":"Improved hybrid method to calculate inductances of permanent magnet synchronous machines with skewed stators based on winding function theory","volume":"2","author":"Gao","year":"2016","journal-title":"Chin. J. Electr. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sun, W., Hang, J., Ding, S., Hu, Q., and Ren, X. (2020, January 7\u201310). Electromagnetic Parameters Analysis of Inter-Turn Short Circuit Fault in DTP-PMSM Based on Finite Element Method. Proceedings of the 8th International Conference on Power Electronics Systems and Applications (PESA), Hong Kong, China.","DOI":"10.1109\/PESA50370.2020.9344033"},{"key":"ref_6","unstructured":"Fu, S., Jianbin, Q., Chen, L., and Chadli, M. (2020). Adaptive fuzzy observer-based fault estimation for a class of nonlinear stochastic hybrid systems. IEEE Trans. Fuzzy Syst., 1."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Saidi, L., Fnaiech, F., Capolino, G.-A., and Henao, H. (2012, January 25\u201328). Stator current bi-spectrum patterns for induction machines multiple-faults detection. Proceedings of the IECON 2012\u201438th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, Canada.","DOI":"10.1109\/IECON.2012.6388975"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1109\/JSYST.2013.2288172","article-title":"Online Detection of Induction Motor\u2019s Stator Winding Short-Circuit Faults","volume":"8","author":"Eftekhari","year":"2014","journal-title":"IEEE Syst. J."},{"key":"ref_9","first-page":"112","article-title":"Application of instantaneous power decomposition technique in induction motors stator fault diagnosis","volume":"5","author":"Hou","year":"2005","journal-title":"Proc. Csee"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1109\/TII.2015.2462315","article-title":"Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis","volume":"11","author":"Keskes","year":"2015","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1109\/TIE.2015.2496900","article-title":"Interturn Fault Diagnosis Strategy for Interior Permanent-Magnet Synchronous Motor of Electric Vehicles Based on Digital Signal Processor","volume":"63","author":"Du","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Y., and Liang, Y. (2015, January 2\u20135). The correlation analysis of PM inter-turn fault based on stator current and vibration signal. Proceedings of the 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China.","DOI":"10.1109\/ICMA.2015.7237747"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nguyen, N.P., Mung, N.X., Thanh Ha, L.N.N., Huynh, T.T., and Hong, S.K. (2020). Finite-Time Attitude Fault Tolerant Control of Quadcopter System via Neural Networks. Mathematics, 8.","DOI":"10.3390\/math8091541"},{"key":"ref_14","first-page":"940076","article-title":"Adaptive Sliding Mode Control for Attitude and Altitude System of a Quadcopter UAV via Neural Network","volume":"9","author":"Nguyen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, J., Liu, J., and Wang, Z. (2021). Convolutional Neural Network for Crowd Counting on Metro Platforms. Symmetry, 13.","DOI":"10.3390\/sym13040703"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hossain, S.M.M., Deb, K., Dhar, P.K., and Koshiba, T. (2021). Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models. Symmetry, 13.","DOI":"10.3390\/sym13030511"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wagner, T., and Sommer, S. (2020, January 24\u201326). Bearing fault detection using deep neural network and weighted ensemble learning for multiple motor phase current sources. Proceedings of the 2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Novi Sad, Serbia.","DOI":"10.1109\/INISTA49547.2020.9194618"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1016\/j.eswa.2010.07.119","article-title":"Fault diagnosis of ball bearings using machine learning methods","volume":"38","author":"Kankar","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TII.2016.2645238","article-title":"Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine","volume":"13","author":"Liu","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.measurement.2016.07.054","article-title":"Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis","volume":"93","author":"Guo","year":"2016","journal-title":"Measurement"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.jsv.2016.05.027","article-title":"Convolutional Neural Network Based Fault Detection for Rotating Machinery","volume":"377","author":"Janssens","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TIM.2018.2847800","article-title":"Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning","volume":"68","author":"Kao","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ress.2013.02.022","article-title":"Failure diagnosis using deep belief learning based health state classification","volume":"115","author":"Tamilselvan","year":"2013","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rodriguez, A.L., Huang, L., Lombard, P., Leconte, V., and Villar, I. (2020, January 23\u201326). Vibration Analysis of a PMSM through FEM Multiphysics Simulation with Experimental Validation. Proceedings of the 2020 International Conference on Electrical Machines (ICEM), Gothenburg, Sweden.","DOI":"10.1109\/ICEM49940.2020.9271070"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.3906\/elk-1601-157","article-title":"Eccentricity fault diagnosis in a permanent magnet synchronous motor under nonstationary speed conditions","volume":"25","author":"Eker","year":"2017","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Krichen, M., Elbouchikhi, E., Benhadj, N., Chaieb, M., Benbouzid, M., and Neji, R. (2020). Motor Current Signature Analysis-Based Permanent Magnet Synchronous Motor Demagnetization Characterization and Detection. Machines, 8.","DOI":"10.3390\/machines8030035"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/1687-5281-2013-29","article-title":"Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis","volume":"2013","author":"Shahriar","year":"2013","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TII.2009.2030793","article-title":"Fault Detection Based on Statistical Multivariate Analysis and Microarray Visualization","volume":"6","author":"Ma","year":"2010","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1109\/TII.2016.2594773","article-title":"Application of Digital Image Processing to Detect Short-Circuit Turns in Power Transformers Using Frequency Response Analysis","volume":"12","author":"Aljohani","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_31","first-page":"1","article-title":"Weighting Rules of Principle Component Extraction Information Criterion","volume":"8","author":"Du","year":"2019","journal-title":"Acta Autom. Sin."},{"key":"ref_32","first-page":"84","article-title":"Image super-resolution reconstruction based on parallel residual convolutional network","volume":"20","author":"Yang","year":"2019","journal-title":"J. Air Force Eng. Univ. (Nat. Sci. Ed.)"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"117367","DOI":"10.1016\/j.conbuildmat.2019.117367","article-title":"Image-based concrete crack detection in tunnels using deep fully convolutional networks","volume":"234","author":"Ren","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","first-page":"27","article-title":"Intelligent recognition of multi-object ferrographic wear particles based on improved YOLO algorithm","volume":"46","author":"Zhang","year":"2021","journal-title":"Lubr. Eng."},{"key":"ref_36","first-page":"1088","article-title":"Motor fault diagnosis method based on an improved one-dimensional convolutional neural network","volume":"40","author":"Ma","year":"2020","journal-title":"J. Beijing Inst. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object Detection With Deep Learning: A Review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_38","first-page":"79","article-title":"Research on Adaptive Learning Rate Algorithm of Deep Learning","volume":"47","author":"Jiang","year":"2019","journal-title":"J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.)"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Feng, X., Gao, X., and Luo, L. (2021). X-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection. Symmetry, 13.","DOI":"10.3390\/sym13040706"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107190","DOI":"10.1016\/j.measurement.2019.107190","article-title":"A novel method of combining nonlinear frequency spectrum and deep learning for complex system fault diagnosis","volume":"151","author":"Chen","year":"2020","journal-title":"Measurement"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, T., Li, Z., Deng, Z., and Hu, B. (2019). Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers. Sensors, 19.","DOI":"10.3390\/s19112504"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3745","DOI":"10.1364\/OL.44.003745","article-title":"Recurrent neural network (RNN) for delay-tolerant repetition-coded (RC) indoor optical wireless communication systems","volume":"44","author":"He","year":"2019","journal-title":"Opt. Lett."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/10\/1844\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:08:45Z","timestamp":1760166525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/10\/1844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,2]]},"references-count":42,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["sym13101844"],"URL":"https:\/\/doi.org\/10.3390\/sym13101844","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2021,10,2]]}}}