Compressor stall characteristics based on variational mode decomposition

Based on the axial flow compressor test bench, performance tests on a two-stage compressor with adjustable inlet guide vanes were conducted under different inlet guide vane angles and different rotational speeds, and the stable working range and stall boundary of the compressor were obtained. Considering that the traditional empirical mode decomposition (EMD) method is prone to frequency aliasing of modal components, the variational mode decomposition (VMD) method has been s adopted to conduct feature recognition and extraction of the compressor stall signal. In response to the phenomenon that VMD overly relies on the number of modes K and the penalty factor α in the extraction of compressor stall characteristics, an improved algorithm based on bionics optimization was adopted. By introducing the leapfrog algorithm to construct an adaptive parameter optimization mechanism, the dynamic optimization of K and α was achieved with the average envelope entropy as the optimization objective, and the advantages of the simulation signal verification parameter optimization VMD method were constructed. Finally, the improved algorithm was applied to the signal analysis of the compressor stall working condition. The results show that compared with the EMD method, the parameter-optimized VMD method can separate the dominant modal components representing the stall characteristics more accurately.