Direction-sensitive vibration feature fusion for early-stage wear detection in water lubricated bearings

Abstract Water-lubricated bearings are critical in ship propulsion, and wear accelerates under low-speed, heavy-load conditions, making early detection essential. Existing studies mainly focus on rolling bearings and employ time- or frequency-domain analyses. While effective for obvious impacts, these methods often fail to capture weak transient signals associated with early wear. Directional dependence is also neglected, although wear responses of water-lubricated stern bearings vary with direction, reducing the sensitivity and interpretability of conventional frameworks. To address these issues, this study proposes a direction-sensitive detection framework that integrates time-domain features with Ensemble Empirical Mode Decomposition (EEMD)-based multi-scale energy features for multi-dimensional evaluation. Features are standardized using Z score normalization and reduced via Principal Component Analysis (PCA), and their contributions quantified using Spearman correlation and SHapley Additive exPlanations (SHAP) values to identify direction-sensitive indicators. Results show that vertical radial Kurtosis features are most sensitive to early wear, while horizontal radial features provide robust supplementary information. High-frequency time–frequency features respond more sensitively to early wear, whereas time-domain features exhibit stronger directional specificity. Based on these findings, a multi-dimensional detection framework is established, with vertical radial vibrations as core early-warning indicators and horizontal radial vibrations as robust supplements, providing a high-sensitivity, adaptable solution for early wear detection of water-lubricated bearings.