集成GA-PSO方法的转子系统多点不平衡量识别

针对传统转子动平衡方法需多次启车确定平衡配重、平衡效率低、平衡成本高的问题,提出了集成遗传算法(genetic algorithm,简称GA)及粒子群算法(particle swarm optimization,简称PSO)的转子多点不平衡量在线识别方法。该方法的核心是将转子不平衡量分解为数目、位置、质量和相位信息,分别获取转子系统理论不平衡响应与实际振动特征,正反问题角度相结合实现转子多点不平衡量的准确识别。首先,采用集成化的GA-PSO优化算法进行不平衡量识别;其次,通过引入正则化思想构造新的目标函数,利用遗传算法获取不平衡数目的稀疏表示,实现不平衡量数目识别;最后,采用粒子群算法进行不平衡量位置、质量和相位识别,通过缩小粒子群算法初值范围,提高不平衡位置、质量和相位识别精度。仿真和转子实验台实验数据的识别结果表明,该方法可以有效对转子不平衡量进行在线预估,并可有效指导现场无试重动平衡,从而降低后续转子系统现场动平衡的成本,提高其平衡效率。

Multi-unbalances Identification of Rotor System Integrated with GA-PSO Method

Traditional balancing procedures need test weights for calculating correction masses, which are of inefficiency and high-cost. Anovel online unbalance identification method for rotor system, which is based on integrated genetic algorithm and particle swarm optimization (GA-PSO), is proposed. The core of this method is to decompose the unbalance of rotor system into number, location, mass and phase. Theoretical unbalance responses and measured vibration are complemented in direct and inverse, and integrated GA-PSO is applied to identify the unbalance. Firstly, a new objective function is constructed by using regularization method. The sparse-representation of unbalanced vector can be acquired by using GA, then the unbalanced number can be identified. Secondly, PSO is applied to identify unbalanced locations, masses and phases, and the accuracy can be enhanced by narrowing the initial value of PSO. The simulation and experiments results show that the proposed methods can predict the rotor unbalance effectively and provide guidance for the dynamics balance without trial weights. The cost of subsequent rotor system dynamics balance can be decreased, and the efficiency can be increased.