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Deep learning and data augmentation for partial discharge detection in electrical machines

  • Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair caseFault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise.show moreshow less

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Metadaten
Author:Andreas RauscherORCiD, Johannes Kaiser, Manoj DevarajuORCiD, Christian EndischORCiD
Language:English
Document Type:Article
Year of first Publication:2024
published in (English):Engineering Applications of Artificial Intelligence
Publisher:Elsevier
Place of publication:Amsterdam
ISSN:1873-6769
ISSN:0952-1976
Volume:133
Issue:Part A
Pages:13
Article Number:108074
Review:peer-review
Open Access:ja
Version:published
URN:urn:nbn:de:bvb:573-45384
Related Identifier:https://doi.org/10.1016/j.engappai.2024.108074
Faculties / Institutes / Organizations:Fakultät Elektro- und Informationstechnik
Institut für Innovative Mobilität (IIMo)
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2024/02/26