基于果蝇算法的转子不平衡参数辨识研究

为识别转子的不平衡参数，建立了转子-轴承有限元模型，在逆问题的识别理论基础上通过理论故障力与等效估计的不平衡力构建目标函数，运用果蝇优化算法进行目标函数优化，从而识别出不平衡参数，并将果蝇算法识别的结果与模拟退火算法、遗传算法的结果进行对比。仿真和实验结果均表明，相比其他两种算法，果蝇算法有更高的识别精度和效率。
Abstract：In order to identify the rotor unbalance parameters of a rotor, a finite element model of the rotor bearing system was established. The objective function, derived from the difference between theoretical loads and the estimated equivalent unbalance forces based on inverse problem theory, was optimized by using a fruit fly algorithm. Parameters were identified when the objective function reached its minimum. The results of the fruit fly algorithm identification were compared with the results identified by the simulated annealing algorithm and the genetic optimization algorithm. The simulation and experimental results show that the fruit fly algorithm is a more accurate and efficient way for identifying unbalance parameters than the remaining other two algorithms.