A multi-source learning method for open-switch fault diagnosis in power converters

• Main Author: Wu, Yuzhi
• Other Authors: Xu Yan
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2025
• Subjects: Engineering; Fault diagnosis; Multi-source domain adaptation; Extreme learning machine; IGBT; Power converter
• Online Access: https://hdl.handle.net/10356/182463
• Institution: Nanyang Technological University

This dissertation explores a novel multi-source domain adaptation extreme learning machine (MDAELM) method for diagnosing open-circuit faults in insulated gate bipolar transistors (IGBTs) within three-phase inverters. Traditional fault diagnosis methods often fail to address challenges arising from data distribution shifts and domain variability in real-world applications. To overcome these limitations, this research employs maximum mean discrepancy (MMD) to align data distributions across domains and introduces a soft-label weighted voting mechanism to enhance classification accuracy. Experimental results demonstrate that MDAELM outperforms conventional methods, such as single-domain extreme learning machines (ELM) and support vector machines (SVM), in terms of fault classification accuracy, robustness, and computational efficiency. This study provides a scalable and effective solution for power converter fault diagnosis, offering potential applications in industrial systems with varying operational conditions.