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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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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