{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:08:48Z","timestamp":1777705728402,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,3,4]]},"abstract":"<jats:p>With the rapid development of new energy vehicles, the reliability and safety of Brushless DC motor drive system, the core component of new energy vehicles, has been widely concerned. The traditional open circuit fault detection method of power electronic converters have the problem of poor feature extraction ability because of inadequate signal processing means, which lead to low recognition accuracy. Therefore, a fault recognition method based on continuous wavelet transform and convolutional neural network (CWT-CNN) is proposed. It can not only adaptively extract features, but also avoid the complexity and uncertainty of artificial feature extraction. The three-phase current signal is converted into time-frequency spectrum by continuous wavelet transform as the input data of AlexNet. At the same time, the changes of time domain and frequency domain under different fault modes are analyzed. Finally, the softmax classifier with Adam optimizer is used to classify the fault features extracted by CNN to realize the state recognition of different fault modes of power electronic converter. The experimental results show that the CWT-CNN model achieves satisfactory fault detection accuracy under different working conditions and different fault modes. The effectiveness and superiority of the proposed method are verified by comparing with other networks.<\/jats:p>","DOI":"10.3233\/jifs-211632","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T13:29:21Z","timestamp":1643722161000},"page":"3537-3549","source":"Crossref","is-referenced-by-count":17,"title":["Fault detection for power electronic converters based on continuous wavelet transform and convolution neural network"],"prefix":"10.1177","volume":"42","author":[{"given":"Quan","family":"Sun","sequence":"first","affiliation":[{"name":"Nanjing Institute of Technology, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianghai","family":"Yu","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Technology, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongsheng","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Technology, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Peng","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Technology, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guodong","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P.R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211632_ref1","doi-asserted-by":"crossref","first-page":"110648","DOI":"10.1016\/j.rser.2020.110648","article-title":"A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects","volume":"137","author":"\u0130nci","year":"2021","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"10.3233\/JIFS-211632_ref2","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s43236-021-00228-6","article-title":"Power electronic converter reliability and prognosis review focusing on power switch module failures","volume":"21","author":"Abuelnaga","year":"2021","journal-title":"Journal of Power Electronics"},{"key":"10.3233\/JIFS-211632_ref3","doi-asserted-by":"crossref","first-page":"116827","DOI":"10.1016\/j.apenergy.2021.116827","article-title":"A high-accuracy switching loss model of SiC MOSFETs in a motor drive for electric vehicles","volume":"291","author":"Ding","year":"2021","journal-title":"Applied Energy"},{"key":"10.3233\/JIFS-211632_ref4","doi-asserted-by":"crossref","first-page":"116819","DOI":"10.1016\/j.apenergy.2021.116819","article-title":"Design and optimization of a liquid cooled heat sink for a motor inverter in electric vehicles","volume":"291","author":"Han","year":"2021","journal-title":"Applied Energy"},{"key":"10.3233\/JIFS-211632_ref5","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1049\/pel2.12098","article-title":"Open-switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach","volume":"14","author":"Gmati","year":"2021","journal-title":"IET Power Electronics"},{"key":"10.3233\/JIFS-211632_ref6","doi-asserted-by":"crossref","first-page":"4490","DOI":"10.1049\/iet-pel.2020.0795","article-title":"ANN design of multiple open-switch fault diagnosis for three-phase PWM converters","volume":"13","author":"Kim","year":"2020","journal-title":"IET Power Electronics"},{"key":"10.3233\/JIFS-211632_ref7","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.egyr.2020.11.273","article-title":"Open-circuit fault diagnosis of NPC inverter IGBT based on independent component analysis and neural network","volume":"6","author":"Hu","year":"2020","journal-title":"Energy Reports"},{"key":"10.3233\/JIFS-211632_ref8","doi-asserted-by":"crossref","first-page":"829","DOI":"10.3390\/en14040829","article-title":"A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles","volume":"14","author":"Yu","year":"2021","journal-title":"Energies"},{"key":"10.3233\/JIFS-211632_ref9","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.3390\/electronics9081251","article-title":"A Hybrid System Model-Based Open-Circuit Fault Diagnosis Method of Three-Phase Voltage-Source Inverters for PMSM Drive Systems","volume":"9","author":"Chen","year":"2020","journal-title":"Electronics"},{"key":"10.3233\/JIFS-211632_ref10","first-page":"676658704","article-title":"A novel nonlinear observer for fault diagnosis of induction motor","volume":"14","author":"Lingzhi","year":"2020","journal-title":"Journal of Algorithms and Computational Technology"},{"key":"10.3233\/JIFS-211632_ref11","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1108\/COMPEL-08-2018-0303","article-title":"A new hybrid analytical model based on winding function theory for analysis of surface mounted permanent magnet motors","volume":"38","author":"Faiz","year":"2019","journal-title":"Compel"},{"key":"10.3233\/JIFS-211632_ref12","doi-asserted-by":"crossref","first-page":"219672","DOI":"10.1109\/ACCESS.2020.3042101","article-title":"A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems","volume":"8","author":"Mansouri","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-211632_ref13","first-page":"1530","article-title":"Effective Random Forest-Based Fault Detection and Diagnosis for Wind Energy Conversion Systems","volume":"1","author":"Fezai","year":"2021","journal-title":"IEEE Sensors Journal"},{"key":"10.3233\/JIFS-211632_ref14","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.renene.2020.01.010","article-title":"Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems","volume":"150","author":"Kouadri","year":"2020","journal-title":"Renewable Energy"},{"key":"10.3233\/JIFS-211632_ref15","doi-asserted-by":"crossref","first-page":"6669006","DOI":"10.1155\/2021\/6669006","article-title":"Compound Fault Diagnosis for Gearbox Based Using of Euclidean Matrix Sample Entropy and One-Dimensional Convolutional Neural Network","volume":"2021","author":"Decai","year":"2021","journal-title":"Shock and Vibration"},{"key":"10.3233\/JIFS-211632_ref16","first-page":"1299","article-title":"Francesco Lannuzzo, Jens Bo Holm-Nielsen","volume":"13","author":"Kiran Kumar","year":"2020","journal-title":"Fault Investigation in Cascaded H-Bridge Multilevel Inverter through Fast Fourier Transform and Artificial Neural Network Approach. Energies"},{"key":"10.3233\/JIFS-211632_ref17","doi-asserted-by":"crossref","first-page":"228","DOI":"10.3390\/make3010011","article-title":"A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors","volume":"3","author":"Alexakos","year":"2021","journal-title":"Machine Learning and Knowledge Extraction"},{"key":"10.3233\/JIFS-211632_ref18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1080\/21642583.2020.1860153","article-title":"A self-Adaptive CNN with PSO for bearing fault diagnosis","volume":"9","author":"Chen","year":"2020","journal-title":"Systems Science & Control Engineering"},{"key":"10.3233\/JIFS-211632_ref19","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1007\/s11760-020-01701-8","article-title":"Bridging the gap between the short-time Fourier transform (STFT), wavelets, the constant-Q transform and multi-resolution STFT","volume":"14","author":"Mateo","year":"2020","journal-title":"Signal, Image and Video Processing."},{"key":"10.3233\/JIFS-211632_ref20","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1073\/pnas.15.2.127","article-title":"The quantum laws and the uncertainty principle of heisenberg","volume":"15","author":"Lewis","year":"1929","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.3233\/JIFS-211632_ref21","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.acha.2020.08.003","article-title":"Uniqueness of STFT phase retrieval for bandlimited functions","volume":"50","author":"Alaifari","year":"2021","journal-title":"Applied and Computational Harmonic Analysis"},{"key":"10.3233\/JIFS-211632_ref22","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proceedings of the IEEE"},{"key":"10.3233\/JIFS-211632_ref23","first-page":"84","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.3233\/JIFS-211632_ref24","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1080\/01431161.2019.1694725","article-title":"Analysis of various optimizers on deep convolutional neural network model in the application of hperspectral remote sensing image classification","volume":"41","author":"Bera","year":"2020","journal-title":"International Journal of Remote Sensing"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211632","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:45:00Z","timestamp":1777455900000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211632"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,4]]},"references-count":24,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211632","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,4]]}}}