A fault diagnosis technique using refined composite attention entropy and BWO-SVM

Rolling bearings are core components of rotating machinery, unexpected faults cause economic losses and safety risks. To solve problems of poor feature discriminability and blind parameter selection in traditional fault diagnosis, this paper proposes a method based on refined composite multi-scale attention entropy (RCMAE) and Beluga whale optimization (BWO) for multi-classification support vector machines (SVM). For feature extraction, RCMAE (avoiding hyper-parameter optimization and capturing subtle multi-scale fault information) is fused with time-domain features to form a discriminative multi-dimensional feature vector. For the diagnostic model, BWO optimizes SVM’s key parameters ( c and g ) to eliminate blind selection impacts, establishing the BWO-SVM model. Verification on Jiangnan University and Huazhong University of Science and Technology bearing datasets shows diagnostic accuracies of 99.2% and 100%, respectively. Comparative experiments with DT, RF, KNN, and LSTM confirm its superior accuracy, stability, and generalization. This study provides a reliable technical solution for engineering applications, supporting condition monitoring and predictive maintenance of rotating machinery in manufacturing, wind power, etc., to reduce costs and improve system reliability.