A parameter-optimized variational mode decomposition method using salp swarm algorithm and its application to acoustic-based detection for internal defects of arc magnets

Abstract

The acoustic-based detection is regarded as an effective way to detect the internal defects of arc magnets. Variational mode decomposition (VMD) has a significant potential to provide a favorable acoustic signal analysis for such detection. However, the performance of VMD heavily depends on the proper parameter setting. The existing optimization methods for determining the optimal VMD parameter setting still expose shortcomings, including slow convergences, excessive iterations, and local optimum traps. Therefore, a parameter-optimized VMD method using the salp swarm algorithm (SSA) is proposed. In this method, the relationship between the VMD parameters and their decomposition performance is quantified as a fitness function, the minimum value of which indicates the optimal parameter setting. SSA is used to search for such a minimum value from the parameter space. With the optimized parameters, each signal can be decomposed accurately into a series of modes representing signal components. The center frequencies are extracted from the selected modes as feature data, and their identification is performed by random forest. The experimental results demonstrated that the detection accuracy is above 98%. The proposed method has superior performance in the VMD parameter optimization as well as the acoustic-based internal defect detection of arc magnets.

Vectorgastrogram: dynamic trajectory and recurrence quantification analysis to assess slow wave vector movement in healthy subjects

“…Signal decomposition thus consists of obtaining the k modes that minimize the function of cost and the sum of the bandwidths to maintain the signal reconstruction. In this work the number of extracted modes was fixed at k = 10, and the penalty term, α = 2000 [ 51 ]. After applying MVMD in the fasting and fed recordings, the mode with a dominant frequency between 2 and 4 cpm was identified as SW fundamental gastric component.…”