A data-driven method for IGBT open-circuit fault diagnosis based on hybrid ensemble learning and sliding-window classification

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 co...

全面介紹

Saved in:
書目詳細資料
Main Authors: Xia, Yang, Xu, Yan, Gou, Bin
其他作者: School of Electrical and Electronic Engineering
格式: Article
語言:English
出版: 2022
主題:
在線閱讀:https://hdl.handle.net/10356/155302
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
機構: Nanyang Technological University
語言: English
實物特徵
總結: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.