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dc.contributor.authorXia, Yangen_US
dc.contributor.authorXu, Yanen_US
dc.contributor.authorGou, Binen_US
dc.identifier.citationXia, Y., Xu, Y. & Gou, B. (2019). A data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classification. IEEE Transactions On Industrial Informatics, 16(8), 5223-5233.
dc.description.abstractIn this article, a novel data-driven method is proposed for open-circuit fault diagnosis of insulated gate bipolar transistor used in three-phase pulsewidth modulation converter. Based on the sampled three-phase current signals, fast Fourier transform and ReliefF algorithm are used to select most correlated features. Then, based on two randomized learning technologies named extreme learning machine and random vector functional link network, a hybrid ensemble learning scheme is proposed for extracting mapping relationship between fault modes and the selected features. Furthermore, in order to achieve an accurate and fast diagnostic performance, a sliding-window classification framework is designed. Finally, parameters in the diagnostic model are optimized by a multiobjective optimization programming model to achieve optimal balance between diagnosis accuracy and speed. At offline testing stage, the overall average diagnostic accuracy can be as high as 99% with the diagnostic time of around one-cycle sampling time. Furthermore, real-time experiments verify its effectiveness and reliability under different operation conditions.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.relation2019-T1- 001-069 (RG75/19)en_US
dc.relation.ispartofIEEE Transactions on Industrial Informaticsen_US
dc.rights© 2019 IEEE. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classificationen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchRolls-Royce@NTU Corporate Laben_US
dc.subject.keywordsHybrid Ensemble Learningen_US
dc.subject.keywordsMultiobjective Optimization Programmingen_US
dc.description.acknowledgementThis work was supported in part by the Ministry of Education (MOE), Republic of Singapore, under Grant AcRF TIER 1 2019-T1- 001-069 (RG75/19), and in part by the National Research Foundation (NRF) of Singapore under Project NRF2018-SR2001-018. The work of Y. Xu was supported by the Nanyang Assistant Professorship from Nanyang Technological University, Singapore. Paper no. TII-19-3957.en_US
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