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...

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Veröffentlicht in:	IEEE transactions on industrial informatics 2020-08, Vol.16 (8), p.5223-5233
Hauptverfasser:	Xia, Yang, Xu, Yan, Gou, Bin
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Algorithms Artificial neural networks Circuit faults Circuits Classification Converters Diagnostic systems Ensemble learning Fast Fourier transformations Fault diagnosis Feature extraction Fourier transforms Hybrid ensemble learning insulated gate bipolar transistor (IGBT) open-circuit fault Insulated gate bipolar transistors Integrated circuit modeling Machine learning Mapping multiobjective optimization programming (MOP) Multiple objective analysis Optimization Phase current Reliability aspects Semiconductor devices Sliding sliding-window classifier
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