Research on Data Analysis and Feature Extraction of Bearing Source Domain Based on Fault Mechanism
International Journal of Science and Engineering Applications · 2025
To address the adaptability issue of bearing fault diagnosis models in cross-domain transfer tasks, this study focuses on source domain data selection, preprocessing, and mechanism-driven feature extraction.The Case Western Reserve University (CWRU) Bearing Data Center dataset is selected as the source domain data, covering four states: normal (N), outer race fault (OR), inner race fault (IR), and ball fault (B).Firstly, interference is eliminated through data preprocessing (unifying the sampling rate, applying band-pass filtering, slicing with sliding windows, and standardization).Secondly, based on the bearing fault mechanism, characteristic frequencies (BPFO, BPFI, BSF) are calculated using geometric parameters and rotational speed estimation.Then, multi-dimensional features are extracted from the time domain, frequency domain, envelope spectrum, and time-frequency domain to comprehensively capture fault information.Finally, the effectiveness of the features is verified using a radial basis function kernel support vector machine (RBF-SVM) with class weights, achieving an accuracy rate of 97.14% on the test set and a macro-average F1 score of 0.9705.The results indicate that the extracted features have good generalization and discrimination, laying a foundation for subsequent target domain transfer diagnosis.