Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155302
Title: A data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classification
Authors: Xia, Yang
Xu, Yan
Gou, Bin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Xia, 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. https://dx.doi.org/10.1109/TII.2019.2949344
Project: 2019-T1- 001-069 (RG75/19)
NRF2018-SR2001-018
TII-19-3957
Journal: IEEE Transactions on Industrial Informatics
Abstract: In 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.
URI: https://hdl.handle.net/10356/155302
ISSN: 1551-3203
DOI: 10.1109/TII.2019.2949344
Rights: © 2019 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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