Acoustic Emission Signal Diagnosis of Rolling Bearing Faults Based on CEEMDAN and MESSA-SVM

Acoustic emission signals generated during rolling bearing failures reflect their health conditions, serving as crucial indicators for to address the limitations in feature extraction and classification accuracy of traditional methods in acoustic emission signal to address the limitations in feature extraction and classification accuracy of traditional methods in acoustic emission signal, this paper proposes a fault diagnosis method based on CEEMDAN and MESSA-SVM. Initially, CEEMDAN is employed to perform multi-scale Initially, CEEMDAN is employed to perform multi-scale decomposition of fault acoustic emission signals, obtaining Intrinsic Mode Function (IMF) components, followed by selecting critical IMF components The IMF components are selected through kurtosis-correlation feature screening. Permutation entropy is then calculated to construct feature vectors. To optimize SVM classifier parameters, a Multi-strategy Sparrow Search Algorithm (MESSA) incorporating Euclidean distance population diversity, Gaussian perturbation strategy, and fitness difference guidance is designed. This algorithm demonstrates excellent global optimization capability in standard test functions. This algorithm demonstrates excellent global optimization capability in standard test functions. Experimental results indicate that the MESSA-SVM classifier achieves 95% accuracy. Through comparative analysis with other algorithms, the advantages of this method over alternatives include the advantages of this method over alternatives are validated, demonstrating its effectiveness and reliability in bearing fault acoustic emission signal diagnosis.