基于快速稀疏辅助分解的机械非平稳振动信号分离方法

Research on separation method of mechanical non-stationary vibration signals based on fast sparsity-assisted decomposition

针对滚动轴承早期故障信号微弱,故障特征难以识别等问题,提出了一种基于基于快速稀疏辅助分解的滚动轴承故障诊断方法。首先,利用时域和频域稀疏度之间的形态区分,构建了快速稀疏分解模型;其次,证明了该模型凸性的充分必要条件;最后,利用非凸正则化器增强脉冲成分的幅值,通过包络分析实现滚动轴承早期微弱故障诊断。仿真实验表明:快速稀疏辅助分解算法可以有效分离出故障信号中的脉冲成分消除噪声的干扰,实现轴承早期微弱故障诊断。

To address the challenges of weak fault signals and difficult fault feature identification in early rolling bearing fault diagnosis,a fault diagnosis method based on fast sparsity-assisted decomposition is proposed.First,a fast sparse decomposition model is constructed by utilizing the morphological difference between time-domain and frequency-domain sparsity.Second,the sufficient and necessary conditions for the convexity of the model are proven.Finally,the non-convex regularizer is employed to enhance the amplitude of the pulse components,and envelope analysis is applied to achieve early fault diagnosis of rolling bearings.Simulation experiments show that the fast sparsity-assisted decomposition algorithm can effectively separate the pulse components from the fault signal,eliminate noise interference,and achieve early weak fault diagnosis of bearings.