针对轴承故障诊断中信号噪声干扰与模型参数寻优难题,提出了一种基于小波分解去噪、能量熵特征提取以及自适应莱维飞行改进麻雀搜索算法优化支持向量机（SVM）的综合诊断方法。首先,利用小波分解对采集到的轴承振动信号进行多尺度去噪处理,在有效抑制噪声的同时保留了信号的主要特征;随后,将去噪后的信号按一定时间段进行分段,并计算各段的能量熵,以获得反映信号局部能量分布的特征向量;针对传统麻雀搜索算法（SSA）在全局搜索能力和局部收敛性方面的不足,引入自适应莱维飞行策略对其进行改进,从而构建出一种优化性能更为优异的算法;最后,利用改进后的算法对SVM的关键参数进行寻优,建立了高精度的轴承故障诊断模型。实验结果表明,该方法在诊断准确率和鲁棒性方面均优于传统方法,为轴承健康监测提供了有效的技术支撑。

To address the challenges of signal noise interference and model parameter optimization in bearing fault diagnosis, this paper proposes a comprehensive diagnostic method based on wavelet decomposition denoising, energy entropy feature extraction, and the adaptive Lévy flight improved sparrow search algorithm (ALF-SSA) optimized support vector machine (SVM). First, wavelet decomposition is used to perform multi-scale denoising on the collected bearing vibration signals, effectively suppressing noise while retaining the main features of the signal. The denoised signals are then segmented into time intervals, and the energy entropy of each segment is calculated to obtain feature vectors that reflect the local energy distribution of the signal. To overcome the limitations of the traditional sparrow search algorithm (SSA) in global search capability and local convergence, this paper introduces the adaptive Lévy flight strategy to improve the SSA, resulting in a more efficient optimization algorithm. Finally, the improved algorithm is used to optimize the key parameters of SVM, establishing a high-precision bearing fault diagnosis model. Experimental results show that the proposed method outperforms traditional methods in both diagnostic accuracy and robustness, providing effective technical support for bearing health monitoring.