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Abstract: The
nonlinear and non-stationary data set can be decomposed into a finite
and often small number of ‘intrinsic mode functions’ by empirical mode
decomposition(EMD). The intrinsic mode functions usually are the fault
signal in fault diagnosis problem. The most serious problem of EMD
method is the end effects due to the spline fitting at the data ends,
i.e. the envelope curve fitted may have wide swings at the data ends if
the ends are not the extremum. The decomposition quality would be
polluted further alone with the decomposition. An improved empirical
mode decomposition based on the time series analysis is derived. The
envelope curve can be well fitted with the predicted extremum at the
data ends from the time series model. It’s useful to eliminate the end
effects. Compared with the wavelet analysis, EMD method is adaptive. An
example from the gearbox signal is given to demonstrate the power of the
proposed method.
Key
words: Empirical
mode decomposition Time series analysis Forecast
CLC No: TN911.7
TH165+.3
国家863高技术研究发展计划(2001AA423240)和国家自然科学基金(59875013)资助项目.
Received 20031025, received in revised form 20040410
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