Improved multi-scale divergence entropy combined with extreme learning machine classifier for rotating machinery fault recognition

Introduction As the core equipment in industrial production, rotating machinery bearings play a critical role. However, traditional feature extraction algorithms for vibration signals are susceptible to noise interference and inaccurate in extracting complex features. Meanwhile, traditional fault classification algorithms face challenges such as high dependence on feature quality and insufficient generalization ability. Methods For vibration signal feature extraction, an improved multi-scale divergence entropy method is proposed. It integrates multi-scale sample entropy and divergence entropy to enhance the discrimination of signal features. For fault classification, a regularized extreme learning machine (ELM) model is developed, where regularization constraints are introduced to avoid pathological matrices. Results When using the refined composite multi-scale divergence entropy for feature extraction, setting the scale to 20 minimized the entropy value and achieved the highest classification accuracy of 98.79%. For the regularized ELM model, adopting the Softplus function as the activation function and setting the neuron number to 17 led to the lowest loss rate and the highest average classification accuracy of 93.98% ± 0.94%. Additionally, the model exhibited a relatively short running time of only 400 ms. Discussion The results indicate that the improved multi-scale divergence entropy effectively enhances the robustness and accuracy of feature extraction under noise interference. The regularized ELM model improves both classification accuracy and computational efficiency compared to traditional algorithms. This proposed method not only advances the classification accuracy of rotating machinery faults but also provides new technical support for machine fault prevention work, demonstrating potential for practical industrial applications in fault diagnosis systems.