An adaptive fault diagnosis method for rolling bearings based on an improved weighted average algorithm in a strong noise background

Abstract 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.