Condition identification of axial piston pumps based on improved VMD and optimal feature selection

An operating condition identification method for axial piston pumps based on improved Variational Mode Decomposition (VMD) and optimal feature selection is proposed to address the low accuracy in complex operating condition identification caused by the non-stationary, nonlinear, and multi-condition feature redundancy of axial piston pump vibration signals. First, a parameter-optimization criterion based on the dual variation rate of the spectral centroid is established to enable adaptive determination of the VMD mode number K . Then, a multi-stage reconstruction strategy is proposed. This strategy combines Pearson Correlation Coefficients (PCC) with Constant False Alarm Rate (CFAR) techniques, employing principal component screening and weak feature restoration to achieve high-fidelity reconstruction while filtering out noise. Subsequently, mixed time-frequency-domain features are extracted, and secondary screening is performed using the ReliefF-PCC cascading strategy. High-discriminative features are selected using the ReliefF algorithm while eliminating redundant, highly correlated features via PCC. Finally, the selected features are input into a Support Vector Machine (SVM) for classification. Experimental results demonstrate that the proposed method overcomes the non-stationary interference in piston pump vibration signals, achieving an identification accuracy of 99.77%. Comparative analysis against EMD-SVM, WT-SVM, and IVMD-ReliefF-PCC-BP models reveals significant advantages in classification accuracy and robustness, validating the effectiveness and engineering feasibility of the proposed model in complex operating condition identification.