A Feature Fusion Method for Pump Unit Fault Signals Based on Composite Index-Optimized HHO-VMD and SDP

Background: Pump unit vibration signals are typically characterized by non-stationarity and nonlinearity, which makes direct extraction of fault-related information from raw one-dimensional signals difficult, especially under small-sample conditions. Methods: To address this issue, a fault diagnosis method is proposed based on Harris Hawks Optimization for Variational Mode Decomposition, composite-index selection, Symmetric Dot Pattern representation, and deep fusion classification. First, the minimum envelope entropy is used as the fitness function, and HHO is employed to optimize VMD parameters for better decomposition. Then, a composite index CI is constructed to rank and select representative modes for reconstruction. The reconstructed modal signals are mapped into two-dimensional images by SDP, and the representation parameters are optimized using SSIM to enhance structural differences among fault states. Results: Experimental results on the bearing dataset and the pump unit fault dataset show that the proposed method outperforms GADF, GASF, and the original SDP method, achieving diagnosis accuracies of 92.69% and 88.94%, respectively. Conclusions: These results indicate that the proposed framework can effectively improve the clarity, stability, and separability of fault features for small-sample fault diagnosis of pump units.