Y. Li, Q. Ma, Bin Wang, Ming Dong, Lemeng Zhang, Jun Peng
{"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}
引用次数: 1
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.