Abstract
This paper provides a broad overview of the adaptive methods for noise reduction used in the analysis of data in the different sensors such as acoustic emissions sensors, power quality signal analysis. The two algorithms are the Empirical Mode Decomposition and the Ensemble Empirical Mode Decomposition. We selected these two algorithms because our focus is on these methods. Firstly, this paper exhibits the inner workings of each algorithm both in the original authors’ intuition and the mathematical model utilized. Next, we discuss the advantages of each of the algorithms based on recent and credible research papers and articles. We also critically dissect the limitations of each algorithm. This paper aims to give a general understanding on these algorithms which we hope will spur more research in improving the field of signal processing in the fiber optic sensor for the oxidised carbon steel.
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Arzaghi, E., Abaei, M.M., Abbassi, R., Garaniya, V., Chin, C., Khan, F.: Risk-based maintenance planning of subsea pipelines through fatigue crack growth monitoring. Eng. Fail. Anal. 79, 928–939 (2017)
Shariatinasab, R., Akbari, M., Rahmani, B.: Application of wavelet analysis in power systems. In: Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology. InTech (2012)
Shi, Y., Zhang, C., Li, R., Cai, M., Jia, G.: Theory and application of magnetic flux leakage pipeline detection. Sensors 15(12), 31036–31055 (2015)
Zhang, H., Feng, Z., Zou, J.: Research on feature extraction and pattern recognition of acoustic signals based on MEMD and approximate entropy. In: 2017 29th Chinese on Control and Decision Conference (CCDC), pp. 4844–4849. IEEE (2017)
Agarwal, M., Jain, R.: Ensemble empirical mode decomposition: an adaptive method for noise reduction. IOSR J. Electron. Commun. Eng. 5, 60–65 (2013)
Zhan, L., Li, C.: A comparative study of empirical mode decomposition-based filtering for impact signal. Entropy 19(1), 13 (2016)
Sun, J., Xiao, Q., Wen, J., Zhang, Y.: Natural gas pipeline leak aperture identification and location based on local mean decomposition analysis. Measurement 79, 147–157 (2016)
Rostami, J., Chen, J., Tse, P.W.: A signal processing approach with a smooth empirical mode decomposition to reveal hidden trace of corrosion in highly contaminated guided wave signals for concrete-covered pipes. Sensors 17(2), 302 (2017)
Saeed, B.S.: De-noising seismic data by Empirical Mode Decomposition (2011)
Honório, B.C.Z., de Matos, M.C., Vidal, A.C.: Progress on empirical mode decomposition-based techniques and its impacts on seismic attribute analysis. Interpretation 5(1), SC17–SC28 (2017)
Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(01), 1–41 (2009)
Xu, J., et al.: A novel denoising method for an acoustic-based system through empirical mode decomposition and an improved fruit fly optimization algorithm. Appl. Sci. 7(3), 215 (2017)
Siracusano, G., et al.: A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform. Mech. Syst. Signal Process. 75, 109–122 (2016)
Adnan, N., et al.: Leak detection in gas pipeline by acoustic and signal processing-a review. In: IOP Conference Series: Materials Science and Engineering 2015. IOP Publishing (2015)
Camarena-Martinez, D., et al.: Novel down sampling empirical mode decomposition approach for power quality analysis. IEEE Trans. Ind. Electron. 63(4), 2369–2378 (2016)
Su, H., Li, H., Chen, Z., Wen, Z.: An approach using ensemble empirical mode decomposition to remove noise from prototypical observations on dam safety. SpringerPlus 5(1), 650 (2016)
Amin, M.M., Ghazali, M.F., PiRemli, M.A., Hamat, A.M.A., Adnan, N.F.: Leak detection in medium density polyethylene (MDPE) pipe using pressure transient method. In: IOP Conference Series: Materials Science and Engineering, vol. 100, no. 1, p. 012007. IOP Publishing (2015)
Li, X., Wei, Q., Qu, Y., Cai, L.: Incipient loose detection of hoops for pipeline based on ensemble empirical mode decomposition and multi-scale entropy and extreme learning machine. In: IOP Conference Series: Materials Science and Engineering, vol. 211, no. 1, p. 012011. IOP Publishing (2017)
Acknowledgments
This work was supported by Development of Intelligent Pipeline Integrity Management System (I-PIMS) Grant Scheme from Universiti Teknologi PETRONAS.
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Jaafar, N.S.M., Aziz, I.A., Jaafar, J., Mahmood, A.K., Gilal, A.R. (2019). A Survey on Signal Processing Methods in Fiber Optic Sensor for Oxidized Carbon Steel. In: Silhavy, R. (eds) Cybernetics and Algorithms in Intelligent Systems . CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-91192-2_2
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DOI: https://doi.org/10.1007/978-3-319-91192-2_2
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