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To address the low diagnostic accuracy of parametric faults in DC-DC converters under noisy environments, a method based on Variational Mode Decomposition (VMD) is proposed. This method extracts features from fault signals and, combined with a simple convolutional neural network, achieves high-precision fault diagnosis of DC-DC converters in strong noise conditions. Experiments show that the proposed VMD method achieves a diagnostic accuracy of 99.98% under noisy conditions, and maintains an accuracy of over 96% even with 30% of the training dataset.

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Diagnosis of Parametric Faults in DC-DC Converters Under Noisy Environments Based on VMD

  • 会议论文

  • 发表日期：

    2025年

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  • Zhou Lu,
  • Yaun-Yuan Jiang,
  • Miao Zhou,
  • Yuqin Wen

摘要

To address the low diagnostic accuracy of parametric faults in DC-DC converters under noisy environments, a method based on Variational Mode Decomposition (VMD) is proposed. This method extracts features from fault signals and, combined with a simple convolutional neural network, achieves high-precision fault diagnosis of DC-DC converters in strong noise conditions. Experiments show that the proposed VMD method achieves a diagnostic accuracy of 99.98% under noisy conditions, and maintains an accuracy of over 96% even with 30% of the training dataset.