A new method for multicomponent signal decomposition based on self-adaptive filtering

Since the empirical mode decomposition (EMD) lacks strict orthogonality, a new method for multicomponent signal decomposition, orthogonal empirical mode decomposition (OEMD), is proposed by this paper. The essential principle of this method is to obtain the intrinsic mode functions (IMFs) and the residue by self-adaptive band-pass filtering. Firstly, the feasibility of OEMD is theoretically analyzed, then its strict orthogonality and completeness is proved, and the orthogonal basis used in OEMD is generated. Secondly, the method of analytical band-pass filtering which preserves perfect band-pass feature in the frequency domain is presented, then two fast algorithms to implement OEMD are proposed, i.e. IMF sequential searching (ISS) algorithm and IMF binary searching (IBS) algorithm. The speed of IBS is faster than that of ISS, whereas IBS algorithm may obtain much more false IMFs than ISS when signals are of complex spectral constitutions. Finally, OEMD is applied to both synthetic signals and mechanical vibration signals, the results show that compared with EMD, OEMD better solves mode aliasing, avoids the occurrence of false mode, is free of end extension, and can be effectively applied to mechanical fault diagnosis.

► Multicomponent signal decomposition based on self-adaptive filtering is proposed.
► This method has strict orthogonality and completeness.
► Two fast algorithms for implementing this method are developed and compared.
► The new proposed method conquers some problems of empirical mode decomposition.