Research on feature extraction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient

Abstract

Abstract Because it is difficult to extract multiple fault features from mechanical equipment under the interference of background noise and the parameters used in variational mode decomposition (VMD) must be determined in advance, a multiple fault separation method based on adaptive variational mode decomposition (AVMD) is proposed in this research to address these issues. Firstly, a novel index, known as the comprehensive impact coefficient (CIC), is established to properly identify the signal’s fault features. Thereafter, the fitness function of the sparrow search algorithm is developed based on the CIC, and the VMD parameters selection problem is solved. Finally, the decomposed modal components are subjected to envelop demodulation analysis, and the failure type of the bearing is assessed through the envelope spectrum. The simulation and experimental results reveal that the AVMD method can effectively separate all single faults from multiple faults, thus accurately diagnosing bearing faults.