Abstract
This paper studies operational pattern analysis and forecasting for industrial systems. To analyze the global change pattern, a novel methodology for extracting the underlying trends of signals is proposed, which is based on the sum of chosen intrinsic mode functions (IMFs) obtained via empirical mode decomposition (EMD). An adaptive strategy for the selection of the appropriate IMFs to form the trend, is proposed. Then, to forecast the change of the trend, Singular Spectrum Analysis (SSA) is applied. Results from experiment trials on an industrial turbine system show that the proposed methodology provides a convenient and effective mechanism for forecasting the trend of the operational pattern. In so doing, it therefore has application to support flexible maintenance scheduling, rather than the traditional use of calendar based maintenance.
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References
Cho, D., Parlar, M.: A survey of maintenance model for multi-unit systems. European Journal of Operational Research 51, 1–23 (1991)
Peng, X., Gui, W., Li, Y., Hu, Z., Wang, L.: Operational Pattern Optimization for Copper Flash Smelting Process Based on Pattern Decomposition of Fuzzy Neural Networks. In: IEEE International Conference on Control and Automation, ICCA, pp. 2328–2333 (2007)
Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C., Liu, H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society London A: Mathematical, Physical and Engineering Sciences 454, 903–995 (1998)
Huang, N., Shen, Z., Long, S.: A new view of nonlinear water waves: the Hilbert spectrum. Annual Reviews of Fluid Mechanics 31, 417–457 (1999)
Wu, Z., Huang, N., Long, S., Peng, C.: On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of National Academy of Sciences 104, 14889–14894 (2007)
Kumaresan, R., Tufts, D.: Data-adaptive principal component signal processing. In: IEEE Proceedings Conference on Decision and Control, Albuquerque, p. 949 (1980)
Moskvina, V., Zhigljavsky, A.: An algorithm based on singular spectrum analysis for change-point detection. Commun. Statistics-Simul. Comput. 32(2), 319–352 (2003)
Baratta, D., Cicioni, G., Masulli, F., Studer, L.: Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Networks 16, 375–387 (2003)
Golyandina, N., Nekrutkin, V., Zhigljavsky, A.: Analysis of Time Series Structure: SSA and related techniques. Chapman and Hall/CRC, New York (2001)
Grimaldi, S.: Linear Parametric Models Applied to Daily Hydrological Series. Journal of Hydrologic Engineering 9(5), 383–391 (2004)
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Yang, Z., Bingham, C., Ling, WK., Zhang, Y., Gallimore, M., Stewart, J. (2012). Unit Operational Pattern Analysis and Forecasting Using EMD and SSA for Industrial Systems. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_38
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DOI: https://doi.org/10.1007/978-3-642-34156-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34155-7
Online ISBN: 978-3-642-34156-4
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