{"title":"Noise Reduction Method for Partial Discharge Fluorescence Fiber Sensors Based on Optimized Empirical Wavelet Transform","authors":"Chengyong Hu;Yi Huang;Chuanlu Deng;Ming Jia;Qi Zhang;Peng Wu;Yuncai Lu;Qun Li;Xiaobei Zhang;Tingyun Wang","doi":"10.1109/JPHOT.2024.3424439","DOIUrl":null,"url":null,"abstract":"A novel self-adaptive denoising method utilizing optimized empirical wavelet transform (EWT) is proposed to enhance the sensitivity of partial discharge (PD) fluorescence fiber sensors. The optimized EWT enhances the spectrum segmentation capability of conventional EWT via spectral kurtosis (SK). The SK at the optimal window length of noisy PD fluorescence signal is calculated to determine compact support of the Fourier spectrum for subsequent signal decomposition. Frequency components with SK value over the statistic threshold are used to rebuild the PD fluorescence signal. Subsequently, residual noise in the reconstructed signal is removed through adaptive wavelet threshold denoising. To evaluate the performance of the proposed method in denoising numerically simulated and experimentally obtained noisy PD fluorescence signals, outcomes are compared to those of the novel adaptive ensemble empirical mode decomposition (NAEEMD) method, EWT method, EWT joint with kurtogram (KEWT) method, and correlation spectral negentropy (CSNE)-based method. Quantitative metrics and running time are used to assess denoising performance and execution efficiency, respectively. Simulated and experimental results demonstrate that the proposed method possesses a superior noise reduction effect compared to the other four methods while restoring the detail of the PD fluorescence signal flooded by serious noise and consuming reduced computational cost.","PeriodicalId":13204,"journal":{"name":"IEEE Photonics Journal","volume":"16 4","pages":"1-9"},"PeriodicalIF":2.1000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10588988","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Journal","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10588988/","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
A novel self-adaptive denoising method utilizing optimized empirical wavelet transform (EWT) is proposed to enhance the sensitivity of partial discharge (PD) fluorescence fiber sensors. The optimized EWT enhances the spectrum segmentation capability of conventional EWT via spectral kurtosis (SK). The SK at the optimal window length of noisy PD fluorescence signal is calculated to determine compact support of the Fourier spectrum for subsequent signal decomposition. Frequency components with SK value over the statistic threshold are used to rebuild the PD fluorescence signal. Subsequently, residual noise in the reconstructed signal is removed through adaptive wavelet threshold denoising. To evaluate the performance of the proposed method in denoising numerically simulated and experimentally obtained noisy PD fluorescence signals, outcomes are compared to those of the novel adaptive ensemble empirical mode decomposition (NAEEMD) method, EWT method, EWT joint with kurtogram (KEWT) method, and correlation spectral negentropy (CSNE)-based method. Quantitative metrics and running time are used to assess denoising performance and execution efficiency, respectively. Simulated and experimental results demonstrate that the proposed method possesses a superior noise reduction effect compared to the other four methods while restoring the detail of the PD fluorescence signal flooded by serious noise and consuming reduced computational cost.
期刊介绍:
Breakthroughs in the generation of light and in its control and utilization have given rise to the field of Photonics, a rapidly expanding area of science and technology with major technological and economic impact. Photonics integrates quantum electronics and optics to accelerate progress in the generation of novel photon sources and in their utilization in emerging applications at the micro and nano scales spanning from the far-infrared/THz to the x-ray region of the electromagnetic spectrum. IEEE Photonics Journal is an online-only journal dedicated to the rapid disclosure of top-quality peer-reviewed research at the forefront of all areas of photonics. Contributions addressing issues ranging from fundamental understanding to emerging technologies and applications are within the scope of the Journal. The Journal includes topics in: Photon sources from far infrared to X-rays, Photonics materials and engineered photonic structures, Integrated optics and optoelectronic, Ultrafast, attosecond, high field and short wavelength photonics, Biophotonics, including DNA photonics, Nanophotonics, Magnetophotonics, Fundamentals of light propagation and interaction; nonlinear effects, Optical data storage, Fiber optics and optical communications devices, systems, and technologies, Micro Opto Electro Mechanical Systems (MOEMS), Microwave photonics, Optical Sensors.