A multi-source learning method for open-switch fault diagnosis in power converters
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...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2025
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/182463 |
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| 總結: | 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. |
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