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• W4285261617 startingPage "13030" @default.
• W4285261617 abstract "Due to the leakage vibration signal of the water-supply pipeline is easily interfered with by background noise, which leads to a significant leakage location error of time delay estimation. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) combined noise reduction method based on cross-spectral analysis is proposed and applied to leak location of water supply pipelines. Firstly, determine the frequency band range of the source leakage signal by cross-spectral analysis of the leakage vibration signal. At the same time, the leakage vibration signal is decomposed into a finite number of intrinsic mode functions (IMFs) using CEEMDAN. Then, the IMF components are filtered according to the effective amplitude ratio criterion, and the filtered components are reconstructed into a virtual noise channel. Finally, the leakage vibration signal and virtual noise channel are unmixed using FastICA to obtain the best estimate of the source leakage signal, which is used to estimate time delay using cross-correlation for determining the leak location. Simulation and experimental results show that the proposed method effectively improves the signal-to-noise ratio (SNR) and time delay estimation accuracy, and reduces leak location error. Compared with cross-correlation, CEEMDAN-ICA based on correlation coefficients, and CEEMDAN based on cross-spectral analysis, the average relative positioning error of the proposed method is respectively reduced by 11.07%, 9.7%, and 1.8%. In addition, the superiority of the proposed method compared with other state-of-the-art noise reduction methods is verified by comparative experiments." @default.
• W4285261617 created "2022-07-14" @default.
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• W4285261617 date "2022-07-01" @default.
• W4285261617 modified "2023-09-27" @default.
• W4285261617 title "Noise Reduction Based on CEEMDAN-ICA and Cross-Spectral Analysis for Leak Location in Water-Supply Pipelines" @default.
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• W4285261617 doi "https://doi.org/10.1109/jsen.2022.3172557" @default.
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