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Signal decomposition is an effective tool to assist the identification ofmodal information in time-domain signals. Two signal decomposition methods,including the empirical wavelet transform (EWT) and Fourier decompositionmethod (FDM), have been developed based on Fourier theory. However, the EWT cansuffer from a mode mixing problem for signals with closely-spaced modes anddecomposition results by FDM can suffer from an inconsistency problem. Anaccurate adaptive signal decomposition method, called the empirical Fourierdecomposition (EFD), is proposed to solve the problems in this work. Theproposed EFD combines the uses of an improved Fourier spectrum segmentationtechnique and an ideal filter bank. The segmentation technique can solve theinconsistency problem by predefining the number of modes in a signal to bedecomposed and filter functions in the ideal filter bank have no transitionphases, which can solve the mode mixing problem. Numerical investigations areconducted to study the accuracy of the EFD. It is shown that the EFD can yieldaccurate and consistent decomposition results for signals with multiplenon-stationary modes and those with closely-spaced modes, compared withdecomposition results by the EWT, FDM, variational mode decomposition andempirical mode decomposition. It is also shown that the EFD can yield accuratetime-frequency representation results and it has the highest computationalefficiency among the compared methods.

Empirical Fourier Decomposition: An Accurate Adaptive Signal Decomposition Method