Reliability Prediction of Engineering System Based on Adaptive Particle Swarm Optimization Support Vector Regression

被引：2
|
作者：
Zhou C. [1 ]
Bai B. [2 ]
Ye N. [1 ]
机构：
[1] School of Mechanical Engineering, Hebei University of Technology, Tianjin
[2] SANY Heavy Industry Co., Ltd., Changsha
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2023年 / 59卷 / 14期
关键词：
industrial robot; particle swarm algorithm; reliability prediction; support vector machine; turbocharger;
D O I：
10.3901/JME.2023.14.328
中图分类号：
学科分类号：
摘要：
Aiming at the problem of low reliability prediction accuracy, a support vector regression prediction model is proposed. In the process of reliability prediction, an adaptive particle swarm optimization algorithm that combines sine mapping and adaptive strategies to update inertia weights is developed. By enhancing the local mining capabilities and global search capabilities of the algorithm, it is improved to a certain extent. The accuracy and convergence efficiency of the particle swarm algorithm are verified. Based on 8 benchmark functions, the proposed algorithm is compared and verified with other particle swarm algorithms. The results show that the adaptive particle swarm optimization algorithm has better search capabilities than other algorithms. On this basis, a new adaptive particle swarm optimization-support vector machine regression hybrid reliability prediction model is proposed to adjust the parameters of support vector regression and predict the reliability of turbochargers and industrial robot systems. The results show that the hybrid model can meet the actual engineering accuracy requirements in terms of reliability prediction. © 2023 Chinese Mechanical Engineering Society. All rights reserved.
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页码：328 / 338
页数：10
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