Construction of a comprehensive degradation index for rolling bearings based on feature weighting and multidimensional scaling analysis

Abstract The health condition of rolling bearings directly impacts the service performance and reliability of the entire rotating system, with degradation indexs serving as critical elements for effective bearing health monitoring. To address the challenges in timely and accurate reflecting the degradation state, this study integrates multi-resolution singular value decomposition (MRSVD), feature weighting and multidimensional scaling (MDS) analysis to construct a comprehensive degradation index for rolling bearings. Time-domain features are initially extracted from bearing vibration signals, followed by correlation analysis using Pearson’s correlation coefficient to select highly correlated features. The MRSVD method is then employed for multi-level decomposition to achieve deep-level feature extraction. The selected features undergo Gini index and Box–Cox sparsity weighting processing to enhance sensitive feature identification capability. Furthermore, MDS is implemented for dimensionality reduction of the weighted features, ultimately enabling the integration of degradation indices into a unified metric, and the comprehensive degradation index is constructed. Experimental results demonstrate that the proposed method achieves superior performance in real-time bearing health monitoring, it significantly improves the accuracy and timeliness of fault detection, thereby providing effective technical support for the maintenance management of rotating machinery.