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.
Highlights
As one of the most critical parts of a permanent magnet motor, the arc magnet is widely used for generating a long-term, stable magnetic field
A new fitness function composed of both the correlation coefficient and energy loss coefficient is designed to fully indicate the signal decomposition effect obtained by Variational mode decomposition (VMD) based on the understanding of the under-decomposition and over-decomposition results
Such a function is able to build a correspondence between the VMD parameters and the signal decomposition results
Summary
According to the literature reviews mentioned above, it appears that VMD combined with SSA is expected to achieve an optimized VMD that can provide proper signal decomposition performance, and RF may contribute a lot to the identification of internal defect features. It is still unclear how they could be correctly integrated and utilized in the acoustic signal analysis to detect internal defects of arc magnets. To address these issues, we proposed a new acoustic signal analysis method combining VMD, SSA, and RF. The experimental results show that the proposed method is able to accurately determine whether the arc magnet has internal defects
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