Abstract
A novel method named residual-variational mode decomposition (RVMD) is proposed in this study to extract bearing fault features accurately. RVMD can determine the number of modes and the balance parameter adaptively, and it has two stages. In the first stage, the signal is decomposed into a series of modes until the correlation coefficient between the raw signal and the decomposition results reaches the threshold. A redefined kurtosis, which can resist the interferences from aperiodic impulse efficiency, is applied to rebuild the ensemble kurtosis index. The mode that has the largest rebuild-ensemble kurtosis, and its neighbors, are kept. By putting the residual signal into the second stage, an iteration process is applied to determine the optimal parameters for variational mode decomposition (VMD). VMD is re-run with the optimal parameters, and the sub-mode filtered with the larger rebuild-ensemble kurtosis is examined by the envelope analysis technology to observe the fault feature. The effectiveness of RVMD is verified by the simulation signal and three experiment signals. Its superiority is shown by comparing it with some existing methods.
Highlights
Rotation equipment has been widely used in modern industries, such as land transportation, ocean shipping, airlift, and some power transmission fields [1,2,3,4]
Kurtosis is redefined on the basis of the number of elements whose values are above the RSM of the squared data to enhance its anti-interference ability
The ensemble kurtosis based on the redefined kurtosis works well to evaluate the ratio of the periodic impulses to the noisy signal
Summary
Rotation equipment has been widely used in modern industries, such as land transportation, ocean shipping, airlift, and some power transmission fields [1,2,3,4]. Li et al [28] proposed an independence-oriented VMD on the basis of the spectrum distribution to detect wheelset-bearing faults Using this method, they determined a modal number using the number of the local maximum of the fast Fourier transform (FFT) spectrum. Lian et al [30] proposed an adaptive VMD on the basis of the permutation entropy, the extreme value of spectrum, and kurtosis of each sub-modal, which is a complex method and has many parameters that need to be determined artificially. A coarse-to-fine process was proposed to determine parameters for VMD [32] In this method, the modal number was set as one, and the noise sub-modal was subtracted in each iteration.
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