基于小波变换图像卷积的局部放电超声信号时延估计算法

Y. Li, Q. Ma, Bin Wang, Ming Dong, Lemeng Zhang, Jun Peng
{"title":"基于小波变换图像卷积的局部放电超声信号时延估计算法","authors":"Y. Li, Q. Ma, Bin Wang, Ming Dong, Lemeng Zhang, Jun Peng","doi":"10.1109/CEIDP55452.2022.9985289","DOIUrl":null,"url":null,"abstract":"In order to explore the time delay estimation methods under low signal-to-noise ratios (SNRs), this paper proposes a method based on the wavelet transform image convolution. First, by applying the wavelet transform to the acquired signal, the time-frequency graphs are analyzed. Then convolve the image to find the time shift corresponding to the maximum value. The time shift product with the sampling interval equals to the estimated value of time delay. For the purpose of verifying the applicability, the simulated PD signals are constructed, and compare the proposed method with two traditional methods–the threshold method and the generalized cross-correlation method. Simulation results show that prediction accuracy increased by 40% under low SNRs, and greatly reduce the mean absolute deviation of time delay. Finally, Experimental results validated the feasibility of the proposed methods. The location distance error is limited to 0. 15m.","PeriodicalId":374945,"journal":{"name":"2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Time Delay Estimation Algorithm of Partial Discharge Ultrasound Signal Based on Wavelet Transform Image Convolution\",\"authors\":\"Y. Li, Q. Ma, Bin Wang, Ming Dong, Lemeng Zhang, Jun Peng\",\"doi\":\"10.1109/CEIDP55452.2022.9985289\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to explore the time delay estimation methods under low signal-to-noise ratios (SNRs), this paper proposes a method based on the wavelet transform image convolution. First, by applying the wavelet transform to the acquired signal, the time-frequency graphs are analyzed. Then convolve the image to find the time shift corresponding to the maximum value. The time shift product with the sampling interval equals to the estimated value of time delay. For the purpose of verifying the applicability, the simulated PD signals are constructed, and compare the proposed method with two traditional methods–the threshold method and the generalized cross-correlation method. Simulation results show that prediction accuracy increased by 40% under low SNRs, and greatly reduce the mean absolute deviation of time delay. Finally, Experimental results validated the feasibility of the proposed methods. The location distance error is limited to 0. 15m.\",\"PeriodicalId\":374945,\"journal\":{\"name\":\"2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEIDP55452.2022.9985289\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEIDP55452.2022.9985289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

为了探索低信噪比下的时延估计方法,本文提出了一种基于小波变换图像卷积的时延估计方法。首先,对采集到的信号进行小波变换,分析时频图。然后对图像进行卷积,求出最大值对应的时移。时移与采样间隔的乘积等于时延的估计值。为了验证该方法的适用性,构造了PD信号仿真,并与传统的阈值法和广义互相关法进行了比较。仿真结果表明,在低信噪比下,预测精度提高了40%,并且大大降低了时延的平均绝对偏差。最后,实验结果验证了所提方法的可行性。定位距离误差被限制为0。15米。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Delay Estimation Algorithm of Partial Discharge Ultrasound Signal Based on Wavelet Transform Image Convolution
In order to explore the time delay estimation methods under low signal-to-noise ratios (SNRs), this paper proposes a method based on the wavelet transform image convolution. First, by applying the wavelet transform to the acquired signal, the time-frequency graphs are analyzed. Then convolve the image to find the time shift corresponding to the maximum value. The time shift product with the sampling interval equals to the estimated value of time delay. For the purpose of verifying the applicability, the simulated PD signals are constructed, and compare the proposed method with two traditional methods–the threshold method and the generalized cross-correlation method. Simulation results show that prediction accuracy increased by 40% under low SNRs, and greatly reduce the mean absolute deviation of time delay. Finally, Experimental results validated the feasibility of the proposed methods. The location distance error is limited to 0. 15m.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信