基于FOA-GRNN方法的工业机器人交叉滚子轴承寿命预测  

Life Prediction of Crossed Roller Bearings for Industrial Robots Based on FOA-GRNN Approach

作　　者：秦亮亮 Qin Liangliang(Huai'an Bioengineering Branch,Jiangsu Union Technical Institute,Huai'an Jiangsu 223200,China)

机构地区：[1]江苏联合职业技术学院淮安生物工程分院,江苏淮安223200

出　　处：《现代工业经济和信息化》2024年第10期124-125,128,共3页Modern Industrial Economy and Informationization

摘　　要：交叉滚子轴承在工业机器人上得到广泛应用,其使用寿命直接影响到工业机器人经济成本。为保证广义回归神经网络(GRNN)的更高预测精度,采用多种群自适应果蝇优化算法(FOA)对其扩展速度进行优化,构建了基于FOA-GRNN方法的工业机器人交叉滚子轴承寿命预测方法。研究结果表明,通过FOA-GRNN方法预测具有较高的结果。相对于单独的FOA和GRNN方法,采用FOA-GRNN方法各项指标均是最小的,验证了FOA优化GRNN方法的有效性,实现了寻优效率与精度的提升。该研究有助于提高工业机器人的运行寿命,具有很高的节能意义。Crossed roller bearings are widely used in industrial robots,and their service life directly affects the economic cost of industrial robots.In order to ensure the higher prediction accuracy of generalised regression neural network(GRNN),multiple swarm adaptive Drosophila optimisation algorithms(FOA)are used to optimise its expansion speed,and a life prediction method for cross roller bearings of industrial robots based on the FOA-GRNN method is constructed.The results of the study show that the prediction by FOA-GRNN method has high results.Compared with the separate FOA and GRNN methods,all the indicators using the FOA-GRNN method are the smallest,which verifies the effectiveness of the FOA-optimised GRNN method,and achieves the improvement of the efficiency and accuracy of the optimisation search.This research helps to improve the operating life of industrial robots and has high significance of energy saving.

关 键 词：工业机器人 交叉滚子轴承 使用寿命 广义回归神经网络 

分 类 号：TH137[机械工程—机械制造及自动化]