基于粒子群优化盲源分离方法的电机滚动轴承复合故障诊断

基于粒子群算法实现容易、精度高、收敛快等优点,将粒子群算法与盲源分离相结合,提出基于粒子群优化的盲源分离方法 (PSO-BSS),并将其应用于电机滚动轴承复合故障诊断中。PSO-BSS方法以峭度的绝对值之和作为目标函数,通过PSO寻找到目标函数的最大值,进而确定最优的分离矩阵。仿真结果表明:PSO-BSS方法能够实现多源复合故障信号的特征分离,并且在分离性能、算法收敛性以及运算速度方面都明显地优于传统的基于遗传算法的机械故障盲源分离方法。最后成功地将PSO-BSS方法应用于实际的滚动轴承内、外圈复合故障盲源分离中,取得良好的分析效果,验证了该方法的工程实用性。

Motor Rolling Bearing Compound Fault Diagnosis Based on Particle Swarm Optimization & Blind Source Separation

Based on the advantages of particle swarm optimization(PSO) that was easily to be realized, high precision and fast convergence, combining PSO and blind source separation(BSS), a new method of mechanical failure blind source separation based on PSO was proposed(PSO-BSS), and the method was applied to rolling bearing compound fault diagnosis. In the PSO-BSS method, the sum of the absolute value of the kurtosis was taken as the target function,the maximum of the target function was sought by PSO,then the optimal separation matrix was determined.The simulation results show that the PSO-BSS method can be used to separate the fault features of multi pile compound fault signal and is significantly superior to the traditional mechanical failure blind source separation based on genetic algorithm (GA-BSS) in separation performance, algorithm convergence and operation speed. Finally, the PSO-BSS method was successfully applied to the actual rolling bearing inner and outer mixing fault blind source separation, a good result was obtained which validated its engineering practicality.