Research on transformer fault voice print information processing based on wavelet packet optimization transform

Lili Wang; Zibin Li; Jianwu Li; Wande Lin; Jiyun Ren; Wanhong Yu; Bingnan Zhao; Nan Chen; Dianzhe Zhao

doi:10.1117/12.2675416

25 May 2023 Research on transformer fault voice print information processing based on wavelet packet optimization transform

Lili Wang, Zibin Li, Jianwu Li, Wande Lin, Jiyun Ren, Wanhong Yu, Bingnan Zhao, Nan Chen, Dianzhe Zhao

Author Affiliations +

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126362N (2023) https://doi.org/10.1117/12.2675416
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

In recent years, more and more attention has been paid to the study of transformer voiceprint. Therefore, this paper mainly studies the denoising technology of transformer voiceprint signal in high noise environment, compares the wavelet packet transform and the wavelet transform, respectively, and finds that the de-noising effect of wavelet packet transform is better than that of wavelet transform. The three-layer decomposition of vowels in the high-frequency part is conducive to the subsequent extraction and identification of fault signals.

Citation Download Citation

Lili Wang, Zibin Li, Jianwu Li, Wande Lin, Jiyun Ren, Wanhong Yu, Bingnan Zhao, Nan Chen, and Dianzhe Zhao "Research on transformer fault voice print information processing based on wavelet packet optimization transform", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126362N (25 May 2023); https://doi.org/10.1117/12.2675416

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