Abstract
Number of existing signal processing methods can be used for extracting useful information. However, receiving desired and eliminating undesired information is yet a significant problem of these methods. Empirical Mode Decomposition (EMD) algorithm shows promising results in comparison to other signal processing methods especially in terms of accuracy. For example, it shows an efficient relationship between signal energy and time frequency distribution. Though, EMD algorithm still has a noise contamination which may compromise the accuracy of the signal processing. It is due to the mode mixing phenomenon in the Intrinsic Mode Function’s (IMF) which causes the undesirable signal with the mix of additional noise. Therefore, it has still a room for the improvements in the selective accuracy of the sensitive IMF after decomposition that can influence the correctness of feature extraction of the oxidized carbon steel. This study has used two datasets to compare the parameters analysis of the Ensemble Empirical Mode Decomposition (EEMD) algorithm for constructing the signal signature.
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References
Underground pipeline corrosion
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)
Gaci, S.: A new Ensemble Empirical Mode Decomposition (EEMD) denoising method for seismic signals. Energy Procedia 97, 84–91 (2016)
Agarwal, M., Jain, R.: Ensemble empirical mode decomposition: an adaptive method for noise reduction. IOSR J. Electron. Commun. Eng 5, 60–65 (2013)
Karkulali, P., Mishra, H., Ukil, A., Dauwels, J.: Leak detection in gas distribution pipelines using acoustic impact monitoring. In: 42nd Annual Conference of the IEEE Industrial Electronics Society, IECON 2016. IEEE (2016)
Datta, S., Sarkar, S.: A review on different pipeline fault detection methods. J. Loss Prev. Process Ind. 41, 97–106 (2016)
Jiao, Y.-L., Shi, H., Wang, X.-H.: Lifting wavelet denoising algorithm for acoustic emission signal. In: 2016 International Conference on Robots and Intelligent System (ICRIS). IEEE (2016)
Adnan, N.F., Ghazali, M.F., Amin, M.M., Hamat, A.M.A.: Leak detection in gas pipeline by acoustic and signal processing-a review. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing (2015)
Fang, Y.-M., Feng, H.-L., Li, J., Li, G.-H.: Stress wave signal denoising using ensemble empirical mode decomposition and instantaneous half period model. Sensors 11(8), 7554–7567 (2011)
Yang, J., Wang, X., Feng, Z., Huang, G.: Research on pattern recognition method of blockage signal in pipeline based on LMD information entropy and ELM. In: Math. Probl. Eng. 2017 (2017)
Kevric, J., Subasi, A.: Comparison of signal decomposition methods in classification of EEG signals For motor-imagery BCI system. Biomed. Sig. Process. Control 31, 398–406 (2017)
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)
Samadi, S., Shamsollahi, M.B.: ECG noise reduction using empirical mode decomposition based combination of instantaneous half period and soft-thresholding. In: 2014 Middle East Conference on Biomedical Engineering (MECBME). IEEE (2014)
Saeed, B.S.: De-noising seismic data by Empirical Mode Decomposition (2011)
Huang, Y., Wang, K., Zhou, Z., Zhou, X., Fang, J.: Stability evaluation of short-circuiting gas metal arc welding based on ensemble empirical mode decomposition. Meas. Sci. Technol. 28(3), 035006 (2017)
Potty, G.R., Miller, J.H.: Acoustic and seismic time series analysis using ensemble empirical mode decomposition. J. Acoust. Soc. Am. 140(4), 3423–3424 (2016)
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)
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)
Xu, J., Wang, Z., Tan, C., Si, L., Liu, X.: 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., Lamonaca, F., Tomasello, R., Garescì, F., La Corte, A., Carnì, D.L., Carpentieri, M., Grimaldi, D., Finocchio, G.: A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform. Mech. Syst. Sig. Process. 75, 109–122 (2016)
Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(01), 1–41 (2009)
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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Mohd Jaafar, N.S., Aziz, I.A., Jaafar, J., Mahmood, A.K., Gilal, A.R. (2019). An Enhance Approach of Filtering to Select Adaptive IMFs of EEMD in Fiber Optic Sensor for Oxidized Carbon Steel. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_24
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DOI: https://doi.org/10.1007/978-3-319-91189-2_24
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