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.