MATEC Web Conf.
Volume 221, 20182018 3rd International Conference on Design and Manufacturing Engineering (ICDME 2018)
|Number of page(s)||6|
|Section||Product Design and Quality Control|
|Published online||29 October 2018|
- Lai, C., Unaccounted for water and the economics of leak detection. Water Supply, 1991. 9(3): p. 4. [Google Scholar]
- Taghvaei, M., S. Beck, and W. Staszewski, Leak detection in pipelines using cepstrum analysis. Measurement Science and Technology, 2006. 17(2): p. 367. [CrossRef] [Google Scholar]
- Liou, C.P., Pipeline leak detection by impulse response extraction. Journal of Fluids Engineering, 1998. 120(4): p. 833–838. [CrossRef] [Google Scholar]
- Ghazali, M.F., Leak detection using instantaneous frequency analysis. 2012, University of Sheffield. [Google Scholar]
- Stephens, M.L., et al. The detection of pipeline blockages using transients in the field. in South Australian Regional Conf. 2002. [Google Scholar]
- Huang, N.E., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 1998. The Royal Society. [Google Scholar]
- Shi, C.-x. and Q.-f. Luo, Hilbert-Huang transform and wavelet analysis of time history signal. Acta Seismologica Sinica, 2003. 16(4): p. 422–429. [CrossRef] [Google Scholar]
- Bin, G., et al., Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 2012. 27: p. 696–711. [CrossRef] [Google Scholar]
- Huang, N.E., Hilbert-Huang transform and its applications. Vol. 16. 2014: World Scientific. [CrossRef] [Google Scholar]
- Manjula, M. and A. Sarma, Comparison of empirical mode decomposition and wavelet based classification of power quality events. Energy Procedia, 2012. 14: p. 1156–1162. [CrossRef] [Google Scholar]
- Ricci, R. and P. Pennacchi, Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mechanical Systems and Signal Processing, 2011. 25(3): p. 821–838. [CrossRef] [Google Scholar]
- Maji, U., M. Mitra, and S. Pal, Automatic Detection of Atrial Fibrillation Using Empirical Mode Decomposition and Statistical Approach. Procedia Technology, 2013. 10:p.45–52. [CrossRef] [Google Scholar]
- Kedadouche, M., M. Thomas, and A. Tahan, Monitoring machines by using a hybrid method combining MED, EMD, and TKEO. Advances in Acoustics and Vibration, 2014. 2014. [CrossRef] [Google Scholar]
- de Souza, D.B., J. Chanussot, and A.-C. Favre. On selecting relevant intrinsic mode functions in empirical mode decomposition: An energy-based approach. in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2014. IEEE. [Google Scholar]
- Nuawi, M.Z., et al., Development of integrated kurtosis-based algorithm for z-filter technique. Journal of applied sciences, 2008. 8(8): p. 1541–1547. [CrossRef] [Google Scholar]
- Rizal, M., et al., A Comparative Study of I-kaz Based Signal Analysis Techniques: Application to Detect Tool Wear during Turning Process. Jurnal Teknologi, 2013. 66(3). [Google Scholar]
- Daubechies, I., Ten lectures on wavelets. Vol. 61. 1992: SIAM. [Google Scholar]
- Wang, Y., et al., Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 2016. 66: p. 679–698. [CrossRef] [Google Scholar]
- Amin, M., et al. Leak detection in medium density polyethylene (MDPE) pipe using pressure transient method. in IOP Conference Series: Materials Science and Engineering. 2015. IOP Publishing. [Google Scholar]
- Ghazali, M., et al., Comparative study of instantaneous frequency based methods for leak detection in pipeline networks. Mechanical Systems and Signal Processing, 2012. 29: p. 187–200. [CrossRef] [Google Scholar]
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