反褶积
负熵
盲反褶积
断层(地质)
包络线(雷达)
特征提取
奇异值分解
计算机科学
算法
灵敏度(控制系统)
干扰(通信)
人工智能
模式识别(心理学)
控制理论(社会学)
数学
独立成分分析
工程类
电子工程
电信
计算机网络
雷达
频道(广播)
控制(管理)
地震学
地质学
作者
Lei Fu,Pengshuai Zhang,Minghui Ding,Sinian Wang,Fang Xu,Zepeng Ma
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:73: 1-14
标识
DOI:10.1109/tim.2023.3331424
摘要
Traditional deconvolution methods struggle to separate fault information under interference from contaminative signals. Although some improved efforts tried, their application is strictly limited to parameter requirements. To overcome this, a composite envelope negentropy deconvolution and reconstruction (CENDR) method is proposed. First, considering the inherent sensitivity to repetitious transient features in the energy domain, composite envelope negentropy (CEN) is utilized as the objective function for deconvolution to enhance the fault features. Then, an adaptive reconstruction method based on CEN and singular value decomposition (SVD) is proposed to reconstruct the filtered signal. The superiority of CENDR lies in its ability to simultaneously consider the periodicity and impulsiveness of fault information. It achieves fault feature extraction under strong interference without relying on strict parameters. Specifically, by utilizing fault characteristic ratio (FCR), the quantitative evaluation reveals that CENDR performs better than the existing optimal deconvolution algorithm, ranging from 28% to 404% under different scenarios.
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