A Strategy Using Variational Mode Decomposition, L-Kurtosis and Minimum Entropy Deconvolution to Detect Mechanical Faults

When faults occur in mechanical components, the faulty information is usually manifested as a series of periodic impulses which correspond to the faulty feature frequencies. However, due to the non-stationary characteristic of the raw vibration signals, the faulty feature frequencies are difficult extracted. In this paper, a novel strategy using variational mode decomposition (VMD), L-Kurtosis and minimum entropy deconvolution (MED) is proposed to detect mechanical faults. First, VMD is employed to decompose the raw vibration signal into a set of intrinsic mode functions (IMFs) to eliminate the interference of the noise. Second, the optimal intrinsic mode function (IMF) which contains the faulty information is determined using L-Kurtosis. Then, the impact characteristic of the periodic impulses in optimal IMF is enhanced through MED. Finally, a Hilbert envelope spectrum analysis is performed to the enhanced signal to extract the faulty feature frequency. In order to illustrate the performance of the proposed strategy, the simulation signal and real experimental signals collected from faulty rolling element bearings and gears are analyzed. The results show that the strategy using the VMD, L-Kurtosis, and MED can detect mechanical component faults effectively.