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Geomagnetic detection method for pipeline defects based on ceemdan and WEP-TEO

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a geomagnetic detection method for pipeline defects using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet energy product (WEP) - Teager energy operator (TEO), which improves detection accuracy and defect identification ability as encountering strong inference noise. The measured signal is first subtly decomposed via CEEMDAN into a series of intrinsic mode functions (IMFs), which are then distinguished by the Hurst exponent to reconstruct the filtered signal. Subsequently, the scale signals are obtained by using gradient calculation and discrete wavelet transform and are then fused by using WEP. Finally, TEO is implemented to enhance defect signal amplitude, completing geomagnetic detection of pipeline defects. The simulation results created by magnetic dipole in a noisy environment, indoor experiment results and field testing results certify that the proposed method outperforms ensemble empirical mode decomposition (EEMD)-gradient, EEMD-WEP-TEO, CEEMDAN-gradient in terms of detection deviation, peak side-lobe ratio (PSLR) and integrated side-lobe ratio (ISLR).
Rocznik
Strony
345--361
Opis fizyczny
Bibliogr. 29 poz., fot., rys., tab., wykr., wzory
Twórcy
autor
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
autor
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
autor
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
autor
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
autor
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
autor
  • Beijing University of Technology, College of Mechanical Engineering and Applied Electronics Technology, 100 Ping Le Yuan, Chaoyang, Beijing 100124, China
Bibliografia
  • [1] Afzal, M., Udpa, S. (2002). Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline. NDT&E Int., 35(7), 449-457.
  • [2] Kim, D., Udpa, L. (2004). Remote field eddy current testing for detection of stress corrosion cracks in gas transmission pipelines. Mater. Lett., 58(15), 2102-2104.
  • [3] Wei, L., Que, P.W., Zhang, Q., Yang, G. (2006). Ultrasonic defect detection of a petroleum pipeline in a viscoelastic medium. Russ. J. Nondestr. Test., 42(10), 692-699.
  • [4] Murav’eva, O.V., Len’kov, S.V., Murashov, S.A. (2016). Torsional waves excited by electromagnetic-acoustic transducers during guided-wave acoustic inspection of pipelines. Acoust. Phys., 62(1),117-124.
  • [5] Hu, B., Yu, R.Q., Liu, J. (2016). Experimental study on the corrosion testing of a buried metal pipeline by transient electromagnetic method. Anti-Corros. Methods Mater., 63(4), 262-268.
  • [6] Krivoi, G.S. (2008). NoPig: An above-ground inspection technique for non-piggable pipelines. Oil. Gas-eur. Mag., 34(3), 122-124.
  • [7] Liao, K.X., Zhang, C. (2011). Standard and application by using non-contact magnetic tomography method for pipeline technical conditions diagnosis. Proc. International Conference on Pipelines and Trenchless Technology, China, 1049-1058.
  • [8] Liao, K.X., Yao, Q.K., Zhang, C. (2011). Principle and technical characteristics of non-contact magnetic tomography method inspection for oil and gas pipeline. Proc. International Conference on Pipelines and Trenchless Technology, China, 1039-1048.
  • [9] Phoong, S.M., Kim, C.W., Vaidyanathan, P.P., Ansari, R. (1995). A new class of two-channel biorthogonal filter banks and wavelet bases. IEEE. T. Signal. Proces., 43(3), 649-665.
  • [10] Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc. A math. Phys. Eng., 454(1971), 903-995.
  • [11] Yang, G., Liu, Y., Wang, Y., Zhu, Z. (2015). EMD interval thresholding denoising based on similarity measure to select relevant modes. Signal. Process., 109(C), 95-109.
  • [12] Kizilkaya, A., Ukte, A., Elbi, M.D. (2015). Statistical multirate high-resolution signal reconstruction using the EMD-IT based denoising approach. Radioengineering, 24(1), 226-232.
  • [13] Humeau-Heurtier, A., Abraham, P., Mahé, G. (2015). Analysis of laser speckle contrast images variability using a novel empirical mode decomposition: Comparison of results with laser doppler flowmetry signals variability. IEEE. T. Med. Imaging., 34(2), 618-627.
  • [14] Wu, Z., Huang, N.E. (2009). Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal., 1(1), 1-41.
  • [15] Chang, K.M., Liu, S.H. (2015). Gaussian noise filtering from ECG by wiener filter and ensemble empirical mode decomposition. J. Sign. Process. Syst., 64(2), 249-264.
  • [16] Han, J., Mirko, V.D.B. Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding. Geophysics., 80(6), KS69-KS80.
  • [17] Yeh, J.R., Shieh, J.S., Huang, N.E. (2010). Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv. Adapt. Data Anal., 2(2), 135-156.
  • [18] Kuai, M., Cheng, G., Pang, Y.S., Li, Y. (2018). Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS. Sensors, 18(3), 782.
  • [19] Dai, S., Niu, D., Li, Y. (2018). Daily peak load forecasting based on complete ensemble empirical mode decomposition with adaptive noise and support vector machine optimized by modified grey wolf optimization algorithm. Energies, 2018, 11(1), 163.
  • [20] Xu, Y., Luo, M.Z., Li, T., Song, G.B. (2017). ECG signal de-noising and baseline wander correction based on CEEMDAN and wavelet threshold. Sensors, 17 (12), 2754.
  • [21] Li, Z.C., Jarvis, R., Nagy, P.B., Dixon, S., Cawley, P. (2017). Experimental and simulation methods to study the Magnetic Tomography Method (MTM) for pipe defect detection. NDT&E Int., 92, 59-66.
  • [22] Lay-Ekuakille, A., Vendramin, G., Trotta, A. (2009). Spectral analysis of leak detection in a zigzag pipeline: A filter diagonalization method-based algorithm application. Measurement, 42(3), 358-367.
  • [23] Lay-Ekuakille, A., Griffo, G., Visconti, P. (2017). Leak detection in waterworks: comparison between STFT and FFT with an overcoming of limitations. Metrol. Meas. Syst., 24(4), 631-644.
  • [24] Song, Q., Ding, W.X., Peng, H., Gu, J., Shuai, J. (2017). Pipe defect detection with remote magnetic inspection and wavelet analysis. Wireless. Pers. Commun., 95(3), 2299-2313.
  • [25] Dubov, A.A., Dubov, A.A., Kolokolnikov, S.M. (2017). Non-contact magnetometric diagnostics of potentially hazardous sections of buried and insulated pipelines susceptible to failure. Weld. World, 61(1), 107-115.
  • [26] Lefebvre, D., Arsenault, H.H., Garcia-Martinez, P., Ferreira, C. (2002). Recognition of unsegmented targets invariant under transformations of intensity. Appl. Opt., 41(29), 6135-6142.
  • [27] Yigit, E., Demirci, S., Ozdemir, C., M Tekbas. (2013). Short-range ground-based synthetic aperture radar imaging: performance comparison between frequency-wavenumber migration and back-projection algorithms. J. Appl. Remote. Sems., 7(1), 073483.
  • [28] Zhan, L. W., Li, C. W. (2016). A comparative study of empirical mode decomposition-based filtering for impact signal. Entropy, 19(1), 13.
  • [29] Ma, W.P., Yin, S.X., Jiang, C.L., Zhang, Y.S. (2017). Variational mode decomposition denoising combined with the Hausdorff distance. Rev. Sci. Instrum., 88(3), 035109.
Uwagi
EN
1. This work was supported by the National Key Research and Development Program of China (2017YFC 0805005-1), Science and Technology Program of Beijing Municipal Education Commission (KZ2018 10005009), Collaborative Innovation Project of Chaoyang District Beijing China (CYXC1709), China Postdoctoral Science Foundation (2018T110018), and “Rixin Scientist” of Beijing University of Technology.
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2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6935de22-865e-415a-ac78-7585ee51fefd
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