A Multi-Indicator Fusion-Based Approach for Fault Feature Selection and Classification of Rolling Bearings

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Concerning the problems of harrowing extraction and poor classification accuracy of fault features in rolling bearing vibration signals, a fault feature selection and classification method based on multi-indicator fusion is proposed. Firstly, the original signal is decomposed through the improved complementary ensemble local mean decomposition (CELMD) method into several physically meaningful product functions (PF) and single residual components; then, the three indicators of kurtosis, correlation coefficient, and K-L divergence are combined to extract the most suitable PF components for signal reconstruction. Ultimately, the reconstructed signal's multi-domain characteristics and entropy value features are retrieved and fed into the LightGBM classifier for classification in order to achieve an intelligent diagnosis of rolling bearing problems. The statistical results demonstrate that the proposed method can efficiently identify the functional PF components and has notable benefits in extracting features from diverse experimental data sets and detecting faults.