GS-ASTFA方法及其在滚动轴承寿命预测中的应用

GS-ASTFA Method and Its Application in life prediction of rolling bearings

自适应最稀疏时频分析(Adaptive and Sparsest Time-Frequency Analysis,ASTFA)方法是一种新的信号分解方法,该方法将信号分解问题转化为优化问题,以得到信号的最稀疏解。优化过程采用高斯-牛顿迭代算法,但高斯-牛顿迭代算法对初值依赖性高,采用黄金分割法(Golden Section,GS)对ASTFA方法进行初值搜索,提出了基于黄金分割搜索初值的ASTFA方法(GS-ASTFA),仿真信号的分析结果验证了改进方法的有效性。继而采用该方法提取了滚动轴承故障特征值,并成功地进行了故障特征值趋势分析和寿命预测。

Adaptive and Sparsest Time-Frequency Analysis （ASTFA） is a new method of signal decomposition. In order to get the sparsest solution of the signal, ASTFA translate signal decomposition into optimization problem. In the optimization procedure, Gauss - Newton iterative algorithm is adopted. However, Gauss - Newton iterative algorithm is sensitive to the choice of initial value. In this paper, the Golden Section （GS） was applied to searching initial value and Golden Section based ASTFA （GS-ASTFA） method are proposed in this paper. The simulation results show that the proposed approach is valid. Furthermore, GS-ASTFA method is applied to rolling bearing eigenvalue extraction, eigenvalue trend analysis and life prediction.