This paper develops an innovative integrated methodology for turbomachinery event detection and prediction by using multivariate noisy operation data. The method seamlessly integrates probabilistic method with multiple advanced analytics techniques, including wavelets and entropy information theory. Wavelets based multi-scale principal component analysis is employed to de-noise the raw data for each tag/variable. Probabilistic principal components analysis is further developed to extract useful information from multiple corrected variables, and entropy information feature is extracted as a precursor of the event, the measure of disorder in a thermodynamic system. The proposed method is so-called Wavelet PCA Entropy. The method considers uncertainty in multivariate data, and provides proof-of-concept of advanced analytics for prediction of challenging events in turbomachinery. The feasibility of the presented methodology is demonstrated with the prediction of combustor lean blow out event and data collected from a real-world gas turbine. This study provides a novel intelligent approach to turbomachinery damage diagnostics and prognostics.
How to Cite
entropy, Diagnostics & Prognostics Methods, probabilistic method, Wavelets, LBO
. Daubechies, I., Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics: Pennsylvania, 1992. doi.org/10.1137/1.9781611970104.
. Mallat, S. (1989), A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7):674–693. DOI: 10.1109/34.192463.
. Coifman, R.R., Wickerhauser, M.V. (1992), Entropy-based algorithms for best basis selection, IEEE Transaction on Information Theory 38(2): 713–718. DOI: 10.1109/18.119732.
. Jiang, X., Mahadevan, S., Adeli, H. (2007), Bayesian wavelet packet denoising for structural system identification, Structural Control and Health Monitoring 14(2): 333–356. DOI: 10.1002/stc.161.
. Aminghafari, M., Cheze, N., Poggi, J.M. (2006), Multivariate de-noising using wavelets and principal component analysis, Computational Statistics & Data Analysis, 50(9): 2381–2398. doi.org/10.1016/j.csda.2004.12.010.
. Mostacci,E., Truntzer, C., Cardot, H., Ducoroy, P. (2010), Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data, Proteomics 10(14): 2564-2572. doi: 10.1002/pmic.200900185.
. Vijaykumar, D.S., Patil, C.G., Ruikar, S.D. (2012), Wavelet based multi-scale principal component analysis for speech enhancement, International Journal of Engineering Trends and Technology, 3(3): 397-400.
. Stoica,P., Moses, R.L., Introduction to Spectral Analysis. New Jersey: Prentice-Hall, Englewood Cliffs, 1997.
. Hotelling, H. (1933), Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24(6): 417–441. doi.org/10.1037/h0071325.
. Joliffe, I.T., Principal Component Analysis, Springer, New York, 2002.
. Tipping, M.E., Bishop, C.M. (1999), Probabilistic principal component analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61(3): 611–622.
. Cooley, W.W., Lohnes, P.R., Multivariate Data Analysis, Wiley & Sons, New York, 1971.
. Tukey, J.W., Exploratory Data Analysis. Addison-Wesley, Reading, Massachusetts, 1977.
. Shannon, C.E. (1948), A mathematical theory of communication, Bell System Technical Journal 27(3): 379–423. DOI: 10.1002/j.1538-7305.1948.tb01338.x.
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