Donglei Zhang

To address the issue that fault features of rolling bearings are difficult to extract effectively under strong background noise interference, leading to challenges in fault diagnosis, an adaptive diagnostic method optimized by an Improved Weighted Averaging Algorithm (IWAA) is proposed. The method incorporates three core techniques: smooth logistic chaotic map initialization, dynamic adaptive position updating, and thinking innovation strategy. These significantly enhance the global optimization capability of the algorithm, with a 59.4% improvement in convergence speed and a 72.4% increase in stability compared to WAA. A joint denoising framework is constructed by integrating Sparse Maximum Harmonic-to-Noise Ratio Deconvolution (SMHD) and Successive Variational Mode Decomposition (SVMD); in this framework, the filter length of SMHD and the penalty factor of SVMD are simultaneously optimized using IWAA, eliminating reliance on manual parameter tuning. An innovative adaptive fluctuation index is introduced, incorporating a dynamic weighting mechanism combining kurtosis and energy entropy to enable intelligent selection of modal components. Verified using public datasets and a custom experimental platform, the method successfully extracts fundamental to 9th and 11th harmonic features of outer race bearing faults under strong noise interference; it significantly enhances impulse periodicity and outperforms mainstream methods in high-order feature recognition, providing a robust diagnostic solution for complex industrial scenarios.

In rolling bearing intelligent fault diagnosis (FD), lightweight models are constrained by issues such as noise interference and the scarcity of fault data, making it challenging to achieve real-time, high-accuracy diagnosis on resource-limited devices. To address these challenges, this study proposes a lightweight model that combines the hierarchical fine-grained decision fusion (HFDF) strategy with an improved EfficientNetV2 architecture (HFDF-EffNetV2). The model optimizes depth and width multiplicity factors to enhance parameter utilization efficiency. It uses pyramidal convolution (PyConv) combined with Fused-MBConv (Fused-MBPyConv) to obtain multi-scale time-frequency information. Additionally, an enhanced MBConv, termed BSMB-Conv-MLCA, integrates subspace blueprint separable convolution (BSConv-S) with mixed local channel attention (MLCA) extract deep-grained fault features. The HFDF strategy outputs confidence in stages and updates weights to prevent the model from falling into local overfitting when handling confusable samples. Experimental results on Case 1 and Case 2 show that HFDF-EffNetV2 achieved 100% accuracy with diagnostic times of 18.67 millisecond (ms) and 17.56 ms, respectively, and 1.85 million (M) parameters. Under noisy conditions, average accuracies reached 98.19% and 85.68%, respectively. Additionally, the model performed well with small samples, yielding accuracies of 98.69% and 97.51%. These results highlight its superior robustness to noise and lightweight performance compared with other advanced models.