基于稀疏算法的大型转子多工况轴心轨迹提纯

Sparse Algorithm-Based Purification of Multi-Condition Axis Trajectory of Large Rotor

针对转子直接合成的轴心轨迹杂乱无章的问题,提出一种基于稀疏分解理论的故障特征频率提取算法(简称稀疏算法),该算法结合旋转机械的信号特征,采用余弦波模型构建过完备字典集,并采用匹配追踪算法求解稀疏系数。采用该算法对受高斯白噪声污染的仿真轴心轨迹进行提纯,得到和理想轴心轨迹几乎完全相同的结果,验证了算法的有效性。最后,将所提出的稀疏算法用于提纯大型滑动轴承试验台主轴升速过程中各工况下的轴心轨迹,成功识别了转子对应的故障类型,并结合动压润滑理论及主轴位置变化规律对故障原因进行了分析。

A fault feature frequency extraction algorithm based on sparse decomposition theory was proposed to solve the problem of chaotic orbit of the rotor directly synthesized. According to the signal characteristics of the rotating machinery, the cosine wave model was used to construct a complete dictionary sets, and the matching pursuit algorithm was used to solve the sparse coefficient. The algorithm was used to purify the simulation axis trajectories polluted by white Gaussian noise. The results were almost identical with the ideal ones, and the validity of the algorithm is verified. Finally, the proposed algorithm was used to purify the multi-condition axis trajectories under various of large sliding bearing test-rig, and the corresponding fault types of the rotor were successfully identified. In addition, combined with the theory of dynamic pressure lubrication and the changing rule of spindle position, the fault causes were analyzed.