Noise Reduction Method for Partial Discharge Fluorescence Fiber Sensors Based on Optimized Empirical Wavelet Transform

IF 2.1 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Photonics Journal Pub Date : 2024-07-08 DOI:10.1109/JPHOT.2024.3424439
Chengyong Hu;Yi Huang;Chuanlu Deng;Ming Jia;Qi Zhang;Peng Wu;Yuncai Lu;Qun Li;Xiaobei Zhang;Tingyun Wang
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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.
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基于优化经验小波变换的部分放电荧光光纤传感器降噪方法
本文提出了一种利用优化经验小波变换(EWT)的新型自适应去噪方法,以提高局部放电(PD)荧光光纤传感器的灵敏度。优化的 EWT 通过频谱峰度(SK)增强了传统 EWT 的频谱分割能力。通过计算噪声局部放电荧光信号最佳窗口长度处的 SK 值,可确定傅立叶频谱的紧凑支持,以便进行后续信号分解。SK 值超过统计阈值的频率成分用于重建 PD 荧光信号。随后,通过自适应小波阈值去噪去除重建信号中的残余噪声。为了评估所提出的方法在数值模拟和实验获得的高噪声 PD 荧光信号去噪方面的性能,将其与新型自适应集合经验模式分解(NAEEMD)方法、EWT 方法、EWT 与峰度图(KEWT)联合方法以及基于相关谱负熵(CSNE)的方法进行了比较。定量指标和运行时间分别用于评估去噪性能和执行效率。模拟和实验结果表明,与其他四种方法相比,所提出的方法具有更优越的降噪效果,同时还原了被严重噪声淹没的 PD 荧光信号的细节,并降低了计算成本。
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来源期刊
IEEE Photonics Journal
IEEE Photonics Journal ENGINEERING, ELECTRICAL & ELECTRONIC-OPTICS
CiteScore
4.50
自引率
8.30%
发文量
489
审稿时长
1.4 months
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.
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