AHC-SCLS-driven cluster quality improvement of hierarchical sample entropy for rotating machinery fault feature extraction

To address insufficient fault discriminability from excessive feature redundancy in rotating machinery analysis, an AHC-SCLS-driven clustering quality enhancement method for hierarchical sample entropy (HSE) is proposed. Hierarchical entropy decomposition first decouples multi-scale entropy features across frequency bands. Agglomerative hierarchical clustering (AHC) then constructs a hierarchical feature tree and reduces dimensionality via redundant attribute merging. A dual-criterion SCLS framework is integrated, where the average Silhouette Coefficient (SC) selects optimal cluster number and the Laplacian Score (LS) screens the most discriminative feature per cluster to form a refined subset. Experiments on the Ottawa University bearing and laboratory gearbox datasets validate the superiority of AHC-SCLS-optimized features. The PSO-SVM classifier achieves 99.29% accuracy on both datasets, other classifiers maintain 95.04%–97.87% accuracy, and the method outperforms mRMR, PCA and other traditional approaches across all classifiers.