Unit Operational Pattern Analysis and Forecasting Using EMD and SSA for Industrial Systems

  • Zhijing Yang
  • Chris Bingham
  • Wing-Kuen Ling
  • Yu Zhang
  • Michael Gallimore
  • Jill Stewart
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)

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.

Keywords

Operational pattern analysis trend extraction empirical mode decomposition signal forecasting singular-spectrum analysis (SSA) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhijing Yang
    • 1
  • Chris Bingham
    • 1
  • Wing-Kuen Ling
    • 1
  • Yu Zhang
    • 1
  • Michael Gallimore
    • 1
  • Jill Stewart
    • 1
  1. 1.School of EngineeringUniversity of LincolnLincolnUK

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