The penalty factors and number of modes in variational mode decomposition (VMD) have to be set empirically. In addition, it is a challenge to select the most useful mode from the multiple mode components decomposed using this method. To solve these problems, an effective gear fault
diagnosis method using variational mode extraction (VME) and envelope analysis is proposed. Due to the blindness in determining the centre frequency of the desired mode, this study proposes a method to select the centre frequency based on the maximum signal-to-noise index among the meshing
frequency and its harmonics. The desired mode obtained according to the selected centre frequency has the maximum signal energy and relatively minimum noise energy. Two case studies verify that the presented approach is effective for gear fault feature extraction. Comparisons with VMD and
empirical mode decomposition (EMD) further highlight the superiority of the method.
The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.