{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:26:03Z","timestamp":1765268763057,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:00:00Z","timestamp":1615507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51975426","51975428"],"award-info":[{"award-number":["51975426","51975428"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Wuhan Science and Technology Project","award":["2019010701011393"],"award-info":[{"award-number":["2019010701011393"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.<\/jats:p>","DOI":"10.3390\/e23030339","type":"journal-article","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T11:56:55Z","timestamp":1615550215000},"page":"339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaowei","family":"Xu","sequence":"first","affiliation":[{"name":"School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Jingyi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Liu","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Zhixiong","family":"Li","sequence":"additional","affiliation":[{"name":"Yonsei Frontier Lab, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"}]},{"given":"Feng","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]},{"given":"Yunbing","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117779","DOI":"10.1016\/j.energy.2020.117779","article-title":"Hybrid electric vehicle electric motors for optimum energy efficiency: A computationally efficient design","volume":"203","author":"Wei","year":"2020","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1016\/j.rser.2016.12.027","article-title":"Development scheme and key technology of an electric vehicle: An overview","volume":"70","author":"Kumar","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10784","DOI":"10.1109\/TPEL.2018.2809668","article-title":"A Comprehensive Analysis of Short-Circuit Current Behavior in PMSM Interturn Short-Circuit Faults","volume":"12","author":"Qi","year":"2018","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_4","first-page":"228","article-title":"Detection of Stator Inter-Turn Short-Circuit Fault in PMSM Based on Improved Wavelet Packet Transform and Signal Fusion","volume":"35","author":"Chen","year":"2020","journal-title":"Trans. China Electron. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1687814020944323","article-title":"Review of intelligent fault diagnosis for permanent magnet synchronous motors in electric vehicles","volume":"12","author":"Xu","year":"2020","journal-title":"Adv. Mech. Eng."},{"key":"ref_6","first-page":"224","article-title":"Bearing Fault Detection for Brushless DC Motors Based on Stator Current","volume":"52","author":"Bian","year":"2020","journal-title":"J. Nanjing Univ. Aeronaut. Astronaut."},{"key":"ref_7","unstructured":"Li, S.S. (2017). Study on Simulation Models and Diagnosis Methods for Multiple Faults od Squirrel Cageinduction Motor, Taiyuan University of Technology."},{"key":"ref_8","first-page":"7","article-title":"Research on Modeling and Simulation of PMSM Common Faults","volume":"48","author":"Wang","year":"2020","journal-title":"Small. Spec. Electron. Mach."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1109\/TIE.2016.2622668","article-title":"A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application","volume":"64","author":"Yan","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Izonin, I., Tkachenko, R., Verhun, V., and Zub, K. (2020). An Approach Towards Missing Data Management Using Improved GRNN-SGTM Ensemble Method. Eng. Sci. Techol. Int. J., 10.","DOI":"10.1016\/j.jestch.2020.10.005"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mehtab, S., and Sen, J. (2020). Stock Price Prediction Using CNN and LSTM- Based Deep Learning Models. IEEE Int. Conf., 447\u2013453.","DOI":"10.1109\/DASA51403.2020.9317207"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bl\u00e1zquez, R.R., and Mu\u00f1oz-Organero, M. (2020). Using Multivariate Outliers from Smartphone Sensor Data to Detect Physical Barriers While Walking in Urban Areas. Technologies, 8.","DOI":"10.3390\/technologies8040058"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"10195","DOI":"10.1007\/s11042-017-5318-1","article-title":"A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing","volume":"77","author":"Varatharajan","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1515\/aoa-2015-0022","article-title":"Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM","volume":"40","author":"Mickiewicza","year":"2015","journal-title":"Arch. Acoust."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neucom.2018.09.001","article-title":"A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer\u2019s disease","volume":"320","author":"Zeng","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105740","DOI":"10.1016\/j.asoc.2019.105740","article-title":"Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring","volume":"84","author":"Abdar","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3235","DOI":"10.1109\/TII.2018.2809730","article-title":"Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling with Variable-Wise Weighted SAE","volume":"14","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_19","first-page":"172","article-title":"Deep learning theory and its application to fault diagnosis of an electric machine","volume":"48","author":"Ding","year":"2020","journal-title":"Power Syst. Protect. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3901\/JME.2015.21.049","article-title":"A Deep Learning-based Method for Machinery Health Monitoring with Big Data","volume":"51","author":"Lei","year":"2015","journal-title":"J. Mech. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data","volume":"72\u201373","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cornia, M., Baraldi, L., Serra, G., and Cucchiara, R. (2016, January 4\u20138). A deep multi-level network for saliency prediction. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900174"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dong, Y., and Liang, G.S. (2019, January 8\u201310). Research and Discussion on Image Recognition and Classification Algorithm Based on Deep Learning. Proceedings of the 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China.","DOI":"10.1109\/MLBDBI48998.2019.00061"},{"key":"ref_24","first-page":"166","article-title":"Transformer fault diagnosis technology based on AdaBoost-RBF algorithm and DSmT","volume":"39","author":"Liu","year":"2019","journal-title":"Electr. Power Autom. Equip."},{"key":"ref_25","first-page":"92","article-title":"Research on Motor Fault Diagnosis Model for Support Vector Machine based on Intelligent Optimization Methods","volume":"37","author":"Zhao","year":"2016","journal-title":"J. Dalian Jiaotong Univ."},{"key":"ref_26","first-page":"166","article-title":"Research on bearing condition monitoring based on deep learning","volume":"35","author":"Guo","year":"2016","journal-title":"J. Vib. Shock."},{"key":"ref_27","first-page":"603","article-title":"Gas Path Fault Diagnosis for Aero-engine Based on Improved Denoising Autoencoder","volume":"39","author":"Hong","year":"2019","journal-title":"J. Vib. Meas. Diagn."},{"key":"ref_28","first-page":"2064","article-title":"Roller bearing fault diagnosis based on improved stacked auto-encoder","volume":"40","author":"Li","year":"2019","journal-title":"Comput. Eng. Des."},{"key":"ref_29","first-page":"294","article-title":"Clutering recognition method of wheat seeds based on deep auto-encoder","volume":"41","author":"Liu","year":"2020","journal-title":"J. Jiangsu Univ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Amimeur, A., Jiang, C., Dou, D.J., Jin, R.M., and Wang, P.W. (2018, January 10\u201313). Density-aware Local Siamese Autoencoder Network Embedding with Autoencoder Graph Clustering. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8621992"},{"key":"ref_31","unstructured":"(2020, September 05). Bearing Data Center. Available online: https:\/\/csegroups.case.edu\/bearingdatacenter\/home."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/3\/339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:35:00Z","timestamp":1760160900000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/3\/339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,12]]},"references-count":31,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["e23030339"],"URL":"https:\/\/doi.org\/10.3390\/e23030339","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2021,3,12]]}}}