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A highly efficient adaptive geomagnetic signal filtering approach using CEEMDAN and salp swarm algorithm

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Abstract

Convenient and helpful defect information within the magnetic field signals of an energy pipeline is often disrupted by external random noises due to its weak nature. Non-destructive testing methods must be developed to accurately and robustly denoise the multi-dimensional magnetic field data of a buried pipeline. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an innovative technique for decomposing signals, showcasing excellent noise reduction capabilities. The efficacy of its filtration process depends on two variables, namely the level of additional noise and the number of ensemble trials. To address this issue, this paper introduces an adaptive geomagnetic signal filtering approach by leveraging the capabilities of both CEEMDAN and the salp swarm algorithm (SSA). CEEMDAN generates a sequence of intrinsic mode functions (IMFs) from the measured geomagnetic signal based on its initial parameters. The Hurst exponent is then applied to distinguish signal IMFs and reproduce the primary filtered signal. SSA fitness, representing its peak value (excluding the zero point) in the normalized autocorrelation function, is utilized. Ultimately, optimal parameters that maximize fitness are determined, leading to the acquisition of their corresponding filtered signal. Comparative tests conducted on multiple simulated signal variants, incorporating varied levels of background noise, indicate that the efficacy of the proposed technique surpasses both EMD denoising strategies and conventional CEEMDAN approaches in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE) assessments. Field testing on the buried energy pipeline is performed to showcase the proposed method’s ability to filter geomagnetic signals, evaluated using the detrended fluctuation analysis (DFA).

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Demirci HE, Karaman M, Bhattacharya S (2021) Behaviour of buried continuous pipelines crossing strike-slip faults: experimental and numerical study. J Natl GAS Sci Eng 92:103980. https://doi.org/10.1016/j.jngse.2021.103980

    Article  Google Scholar 

  2. Biezma MV, Andrés MA, Agudo D, Briz E (2020) Most fatal oil and gas pipeline accidents through history: a lessons learned approach. Eng Fail Anal 110:104446. https://doi.org/10.1016/j.engfailanal.2020.104446

    Article  Google Scholar 

  3. Pesinis K, Tee KF (2018) Bayesian analysis of small probability incidents for corroding energy pipelines. Eng Struct 165:264–277. https://doi.org/10.1016/j.engstruct.2018.03.038

    Article  Google Scholar 

  4. Zelmati D, Bouledroua O, Ghelloudj O, Amirat A, Djukic MB (2022) A probabilistic approach to estimate the remaining life and reliability of corroded pipelines. J Natl Gas Sci Eng 99:104387. https://doi.org/10.1016/j.jngse.2021.104387

    Article  Google Scholar 

  5. Tee KF, Wordu AH (2020) Burst strength analysis of pressurized steel pipelines with corrosion and gouge defects. Eng Fail Anal 108:104347. https://doi.org/10.1016/j.engfailanal.2019.104347

    Article  Google Scholar 

  6. Khemis A, Chaouche AH, Athmani A, Tee KF (2016) Uncertainty effects of soil and structural properties on the buckling of flexible Pipes shallowly buried in Winkler foundation. Str Eng Mech 59:39–759. https://doi.org/10.12989/sem.2016.59.4.739

  7. Taleb-Berrouane M, Khan F, Hawboldt K (2021) Corrosion risk assessment using adaptive bow-tie (ABT) analysis. Reliab Eng Syst Saf 214:107731. https://doi.org/10.1016/j.ress.2021.107731

    Article  Google Scholar 

  8. Ebenuwa AU, Tee KF (2019) Reliability estimation of buried steel Pipes subjected to seismic effect. Transport Geotechn 20:100242. https://doi.org/10.1016/j.trgeo.2019.100242

    Article  Google Scholar 

  9. Sharma VB, Tewari S, Biswas S, Sharma A (2023) A comprehensive study of techniques utilized for structural health monitoring of oil and gas pipelines. Str Health Monitor. https://doi.org/10.1177/14759217231183715

    Article  Google Scholar 

  10. Tran VQ, Le DV, Yntema DR, Havinga PJ (2021) A review of inspection methods for continuously monitoring PVC drinking water mains. IEEE Internet Things J 9(16):14336–14354. https://doi.org/10.1109/JIOT.2021.3077246

    Article  Google Scholar 

  11. Niu X, Tee KF, Marques HR (2021) Superposition model of mode shapes composed of travelling torsional guided waves excited by multiple circular transducer arrays in pipes. Ultrasonics 116:106507. https://doi.org/10.1016/j.ultras.2021.106507

    Article  Google Scholar 

  12. Liu L, Yang L, Gao S (2022) Propagation characteristics of magnetic tomography method detection signals of oil and gas pipelines based on boundary conditions. Sensors 22(16):6065. https://doi.org/10.3390/s22166065

    Article  Google Scholar 

  13. Zhao Y, Wang X, Sun T, Chen Y, Yang L, Zhang T, Ju H (2021) Non-contact harmonic magnetic field detection for parallel steel pipeline localization and defects recognition. Measurement 180:109534. https://doi.org/10.1016/j.measurement.2021.109534

    Article  Google Scholar 

  14. He T, Liao K, Tang J, He G, Deng S, Qin M (2023) Quantitative study of sensor-pipe distance in noncontact magnetic detection of ferromagnetic pipelines. IEEE Sens J 23(7):7879–7894. https://doi.org/10.1016/j.measurement.2021.109534

    Article  Google Scholar 

  15. He G, He T, Liao K, Deng S, Chen D (2022) Experimental and numerical analysis of non-contact magnetic detecting signal of girth welds on steel pipelines. ISA Trans 125:681–698. https://doi.org/10.1016/j.isatra.2021.06.006

    Article  Google Scholar 

  16. Liu L, Yang L, Gao S (2022) Spatial propagation law of magnetic memory signals detected by using magnetic tomography method. IEEE Access 10:64106–64113. https://doi.org/10.1109/ACCESS.2022.3183004

    Article  Google Scholar 

  17. Yang J, Zhao K, Yu X, Yan Y, He Z, Lai Y, Zhou Y (2022) Crack classification of fiber-reinforced backfill based on Gaussian mixed moving average filtering method. Cement Concr Compos 134:104740. https://doi.org/10.1016/j.cemconcomp.2022.104740

    Article  Google Scholar 

  18. Chang SY, Wu HC (2022) Tensor wiener filter. IEEE Trans Signal Process 70:410–422. https://doi.org/10.1109/TSP.2022.3140722

    Article  MathSciNet  Google Scholar 

  19. Dogariu LM, Benesty J, Paleologu C, Ciochină S (2021) An insightful overview of the Wiener Filter for system identification. Appl Sci 11(17):7774. https://doi.org/10.3390/app11177774

    Article  Google Scholar 

  20. Sidi Yakoub M, Selouani SA, Zaidi BF, Bouchair A (2020) Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network. EURASIP J Audio Speech Music Process 2020(1):1–7. https://doi.org/10.1186/s13636-019-0169-5

    Article  Google Scholar 

  21. Ahmed A, Serrestou Y, Raoof K, Diouris JF (2022) Empirical mode decomposition-based feature extraction for environmental sound classification. Sensors 22(20):7717. https://doi.org/10.3390/s22207717

    Article  Google Scholar 

  22. Malik SA, Parah SA, Malik BA (2022) Power line noise and baseline wander removal from ECG signals using empirical mode decomposition and lifting wavelet transform technique. Heal Technol 12(4):745–756. https://doi.org/10.1007/s12553-022-00662-x

    Article  Google Scholar 

  23. Zhou W, Feng Z, Xu YF, Wang X, Lv H (2022) Empirical Fourier decomposition: an accurate signal decomposition method for nonlinear and non-stationary time series analysis. Mech Syst Signal Process 163:108155. https://doi.org/10.1016/j.ymssp.2021.108155

    Article  Google Scholar 

  24. Gao S, Wang Q, Zhang Y (2021) Rolling bearing fault diagnosis based on CEEMDAN and refined composite multiscale fuzzy entropy. IEEE Trans Instrum Meas 70:1–8. https://doi.org/10.1109/TIM.2021.3072138

    Article  Google Scholar 

  25. Shu-wen D, Lu-jun L, Qing-qu W, Kang-le W, Peng-zhan C (2020) Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method. Measurement 161:107865. https://doi.org/10.1016/j.measurement.2020.107865

    Article  Google Scholar 

  26. Liu H, Fang H, Yu X, Wang F, Yang X, Li S (2022) Leak location study of water pipeline based on CEEMDAN-CC at low SNR. Measurement 203:111914. https://doi.org/10.1016/j.measurement.2022.111914

    Article  Google Scholar 

  27. Shi M, Zhao H, Huang Z, Liu Q (2019) Signal extraction using complementary ensemble empirical mode in pipeline magnetic flux leakage nondestructive evaluation. Rev Scient Instrum. https://doi.org/10.1063/1.5089475

    Article  Google Scholar 

  28. Li S, Cai M, Han M, Dai Z (2022) Noise reduction based on CEEMDAN-ICA and cross-spectral analysis for leak location in water-supply pipelines. IEEE Sens J 22(13):13030–13042. https://doi.org/10.1109/JSEN.2022.3172557

    Article  Google Scholar 

  29. Teja K, Tiwari R, Mohanty S (2020) Adaptive denoising of ECG using EMD, EEMD and CEEMDAN signal processing techniques. In Journal of Physics: Conference Series (Vol. 1706, No. 1, p. 012077). IOP Publishing. https://doi.org/10.1088/1742-6596/1706/1/012077

  30. Ge H, Chen G, Yu H, Chen H, An F (2018) Theoretical analysis of empirical mode decomposition. Symmetry 10(11):623. https://doi.org/10.3390/sym10110623

    Article  Google Scholar 

  31. Klionskiy D, Kupriyanov M, Kaplun D (2017) Signal denoising based on empirical mode decomposition. J Vibroeng 19(7):5560–5570. https://doi.org/10.21595/jve.2017.19239

  32. Xu Y, Luo M, Li T, Song G (2017) ECG signal de-noising and baseline wander correction based on CEEMDAN and wavelet threshold. Sensors 17:2754. https://doi.org/10.3390/s17122754

    Article  Google Scholar 

  33. Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144–4147). IEEE. https://doi.org/10.1109/ICASSP.2011.5947265

  34. 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 11:163. https://doi.org/10.3390/en11010163

    Article  Google Scholar 

  35. Duan D, Ma H, Yan Y, Yang Q (2022) A fault diagnosis scheme using hurst exponent for metal particle faults in gil/gis. Sensors 22(3):862. https://doi.org/10.3390/s22030862

    Article  Google Scholar 

  36. Hussien AG (2022) An enhanced opposition-based salp swarm algorithm for global optimization and engineering problems. J Ambient Intell Humaniz Comput 13(1):129–150. https://doi.org/10.1007/s12652-021-02892-9

    Article  Google Scholar 

  37. Mirjalili S et al (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  38. Gang Y, Yingtang Z, Hongbo F, Zhining L, Guoquan R (2016) Detection, localization and classification of multiple dipole-like magnetic sources using magnetic gradient tensor data. J Appl Geophys 128:131–139. https://doi.org/10.1016/j.jappgeo.2016.03.022

    Article  Google Scholar 

  39. Song Q, Ding W, Peng H, Shuai J, Wang B (2017) A new magnetic testing technology based on magnetic gradient tensor theory. Insight – Non-Destruct Test Condit Monitor 59(6), 325–329. https://doi.org/10.1784/insi.2017.59.6.325

  40. Li C, Zhan L, Shen L (2015) Friction signal denoising using complete ensemble EMD with adaptive noise and mutual information. Entropy 17(9):5965–5979. https://doi.org/10.3390/e17095965

    Article  MathSciNet  Google Scholar 

  41. Huang W, Cai N, Xie W, Ye Q, Yang Z (2015) ECG baseline wander correction based on ensemble empirical mode decomposition with complementary adaptive noise. J Med Imag Health Inform 5(8):1796–1799. https://doi.org/10.1166/jmihi.2015.1647

    Article  Google Scholar 

  42. Li J, Zhang X, Tang J (2020) Noise suppression for magnetotelluric using variational mode decomposition and detrended fluctuation analysis. J Appl Geophys 180:104127. https://doi.org/10.1016/j.jappgeo.2020.104127

    Article  Google Scholar 

  43. Zhan L, Li C (2016) A comparative study of empirical mode decomposition-based filtering for impact signal. Entropy 19:13. https://doi.org/10.3390/e19010013

    Article  Google Scholar 

  44. Lu J, Yue J, Zhu L, Wang D, Li G (2021) An improved variational mode decomposition method based on the optimization of salp swarm algorithm used for denoising of natural gas pipeline leakage signal. Measurement 185:110107. https://doi.org/10.1016/j.measurement.2021.110107

    Article  Google Scholar 

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Authors and Affiliations

  1. Anji Liangshan Industrial Operation Services Co., Ltd, Huzhou, Zhejiang Province, People’s Republic of China

    Zia Ullah

  2. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, People’s Republic of China

    Zia Ullah

  3. Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia

    Kong Fah Tee

  4. Interdisciplinary Research Center for Construction and Building Materials, KFUPM, 31261, Dhahran, Saudi Arabia

    Kong Fah Tee

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  1. Zia Ullah
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Ullah, Z., Tee, K.F. A highly efficient adaptive geomagnetic signal filtering approach using CEEMDAN and salp swarm algorithm. J Civil Struct Health Monit (2024). https://doi.org/10.1007/s13349-024-00800-1

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