Raymond, Wong Jee Keen and Illias, Hazlee Azil and Mokhlis, Hazlie (2022) Novel data augmentation for improved insulation fault diagnosis under nonideal condition. Expert Systems with Applications, 209. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2022.118390.
Full text not available from this repository.Abstract
Insulation fault diagnosis is essential because faulty insulation will lead to the interruption of power delivery. Partial discharge (PD) pattern classification is widely used in insulation diagnosis to identify incipient fault before catastrophic failure occurs. PD classification systems improved significantly due to the emergence of advanced machine learning techniques such as deep learning algorithms. However, classification systems trained in ideal noise-free condition suffer severe performance degradation when tested on-site where the condition is nonideal due to the presence of noise contamination. In this work, a novel data augmentation technique was implemented where simulated noise was added to create augmented data. The augmented data was used in tandem with ideal condition PD data during the training phase of four well known convolutional neural networks (CNN). For a realistic performance evaluation, the classification system was trained with ideal noise-free data and augmented data but tested with data overlapped with actual measured noise representing nonideal condition via K-fold cross-validation. The results showed that the proposed data augmentation technique improved the performance of CNN under nonideal condition by 15.83%-29.05% without compromising its performance under ideal condition.
| Item Type: | Article |
|---|---|
| Funders: | Malaysia Ministry of Higher Education and Universiti Malaya [H-16001-D00048] [GPF077A-2018], Impact-Oriented Interdisciplinary Research Grant [IIRG001B-2020IISS], UM Living Labs Grant [LL046-2019], SATU Joint Research Scheme Program |
| Uncontrolled Keywords: | Cable insulation; Partial discharge; Data augmentation; Convolutional neural network |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 13 Sep 2023 03:31 |
| Last Modified: | 13 Sep 2023 03:31 |
| URI: | http://eprints.um.edu.my/id/eprint/41195 |
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