机械与电子2024,Vol.42Issue(10) :76-80.

基于粒子群卷积算法的转杆机械损伤预测研究

Study on Prediction of Rod Mechanical Damage Based on Particle Population Convolution Algorithm

徐立 元春波 徐敏 邵坚铭 冯海 王安琪
机械与电子2024,Vol.42Issue(10) :76-80.

基于粒子群卷积算法的转杆机械损伤预测研究

Study on Prediction of Rod Mechanical Damage Based on Particle Population Convolution Algorithm

徐立 1元春波 1徐敏 1邵坚铭 1冯海 1王安琪1
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作者信息

  • 1. 浙江中烟工业有限责任公司宁波卷烟厂,浙江宁波 315040
  • 折叠

摘要

针对通用旋转机械中转杆在实际使用过程中的机械损伤现象,传统的预测方法依赖于经验公式和简单的统计模型,难以准确捕捉转杆在实际工作环境下复杂的应力状态和损伤演化过程,进而这些方法在预测精度上存在局限性,难以实现早期损伤预警和寿命预测.因此,结合卷积神经网络和粒子群算法,提出一种基于粒子群卷积算法下转杆的机械损伤预测.通过与实际的机械损伤对比,可实现机械损伤预测避免网络陷入局部最优,提高了计算效率和预测准确性,满足实际工程中杆机械损伤预测的需求.

Abstract

There are mechanical damage phenomena in the actual use of universal rotating machinery rotating rod.The traditional prediction methods rely on empirical formulas and simple statistical models,and it is difficult to accurately capture the complex stress state and damage evolution process of the rota-ting rod in the actual working environment.Therefore,these methods have limitations in the prediction ac-curacy,and it is difficult to achieve early damage warning and life prediction.Combined with convolutional neural network and particle swarm optimization algorithm,a kind of mechanical damage prediction of rota-ting rod based on particle swarm optimization algorithm was proposed.By comparing with actual mechani-cal damage,mechanical damage prediction can be realized to avoid the network falling into local optimal,and the calculation efficiency and prediction accuracy are improved,meeting the needs of mechanical dam-age prediction of rod in practical engineering.

关键词

粒子群卷积算法/转杆/机械损伤/预测

Key words

particle group convolution algorithm/rotation bar/mechanical damage/prediction

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出版年

2024
机械与电子
中国机械工业联合会科技工作部 机械与电子杂志社

机械与电子

CSTPCD
影响因子:0.243
ISSN:1001-2257
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