基于自适应权值粒子群算法的滑动轴承优化设计  

Optimal Design of Sliding Bearings Based on Adaptive Particle Swarm Optimization

作　　者：李盼 LI Pan(Hunan Urban Construction College,Xiangtan 411101)

机构地区：[1]湖南城建职业技术学院,湘潭411101

出　　处：《现代制造技术与装备》2023年第1期70-74,共5页Modern Manufacturing Technology and Equipment

摘　　要：针对电机轴承的优化设计问题,选用自适应权值粒子群算法(Adaptive Partical Swarm Optimization,APSO),以轴承宽径比、轴承相对间隙以及润滑油动力黏度为设计参数,根据润滑特性计算得到的范围,采用合理约束条件,确立承载能力、轴承功耗以及轴承温升为目标函数,在分别进行单目标函数优化后,采用加权方法建立多目标函数模型进行优化设计。将得到的优化设计结果与常规设计结果相比,发现采用智能算法优化设计得到的轴承性能得到了显著提高。该优化设计方法相比于常规设计方法,避免了经验设计存在的设计盲目性,对于后续轴承的设计特别是结构设计可以提供较好的指导作用。Aiming at the optimization design problem of motor bearings, this paper chooses Adaptive Partical Swarm Optimization(APSO), takes bearing width-to-diameter ratio, bearing relative clearance and lubricating oil dynamic viscosity as design parameters, and adopts reasonable range according to the calculated range of lubrication characteristics. Constraint conditions, establish load-carrying capacity, bearing power consumption and bearing temperature rise as objective functions. After optimizing the single objective function, a weighted method is used to establish a multi-objective function model for optimization design. Then the optimized design results obtained are compared with the conventional design results, and it is found that the performance of the bearing optimized by the intelligent algorithm has been significantly improved. Compared with conventional design methods, the optimization design method avoids the design blindness of empirical design, and can provide better guidance for subsequent bearing design, especially in structural design.

关 键 词：滑动轴承 自适应权值粒子群算法(APSO) 多目标函数 优化设计 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH133.31[自动化与计算机技术—控制科学与工程]