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The Optimization of Balancing Least Squares Influence Coefficient Method Based on Particle Swarm Optimization

机译:基于粒子群算法的平衡最小二乘影响系数法优化

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摘要

On the basis of the rotor dynamic balancing theory, the rotor balancing least squares influence coefficient method has been discussed in detail in this paper. In order to solve the problem that the residual vibration in some of the measuring point and the balancing weight are comparatively large in the balance process with least squares influence coefficient method, particle swarm optimization algorithm with cross-factor, which is an improved swarm intelligence algorithm, is introduced into rotor balancing least squares influence coefficient method. Theoretical analysis and numerical examples show that the algorithm has a good convergence, with reducing the balancing weight and residual vibration effectively compared with basic least squares influence coefficient method. The result can achieve better balance effect in the rotor balancing process.
机译:本文基于转子动平衡理论,详细讨论了转子平衡最小二乘影响系数法。为了解决最小二乘影响系数法在平衡过程中某些测量点的残余振动和平衡重较大的问题,提出了一种基于交叉因子的粒子群优化算法,它是一种改进的群体智能算法。引入转子平衡最小二乘影响系数法。理论分析和数值算例表明,与基本最小二乘影响系数法相比,该算法具有较好的收敛性,有效降低了平衡权重和残余振动。结果可以在转子平衡过程中达到更​​好的平衡效果。

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