Adaptive bandwidth Fourier decomposition(ABFD)is an analysis method for non-stationary signals based on bandwidth optimization.However,taking the overlapping degree of central frequencies as the decomposition termination condition will yield many redundant modes in practical applications,which will reduce the decomposition efficiency and in-crease the burden for subsequent analysis.Therefore,an efficient bandwidth Fourier decomposition(EBFD)is proposed for bearing fault diagnosis.In this method,a weighted kurtosis guided fast-stop criterion is constructed,which can efficiently de-termine the optimal number of decomposed modes and avoid the interference of a large number of redundant components.Further,the target fault component is located according to the weighted kurtosis index to realize bearing fault diagnosis.Analysis results of rolling bearing fault test signal show that the proposed method can effectively terminate the decomposi-tion process,obtain all potential modes and accurately locate fault components;The normalized frequency-to-energy ratio of fault components extracted by EBFD and ABFD is 1.0,the time required for EBFD operation is 3.3 s,and the decomposition speed is 33.8 s higher than that of ABFD;Compared with other signal analysis methods,the proposed method can more accu-rately identify the bearing fault characteristics.
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