基于改进DeepLabv3+的烧结机车轮摆动检测算法研究
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作者单位:

内蒙古科技大学 自动化与电气工程学院,内蒙古 包头 014010

作者简介:

张昊(1996—),男,硕士研究生,从事计算机视觉、故障检测方面的研究。

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中图分类号:

TF046.4;TP18;TP391.41

基金项目:

内蒙古自治区科技计划资助项目( 2021GG0045) ; 内蒙古自治区高等学校科学研究项目( NJZY21400)


Research on wheel swing detection algorithm of sintering machine based on improved DeepLabv3 +
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School of Automation and Electrical Engineering,Inner Mongolia University of Science & Technology,Baotou 014010 ,Inner Mongolia,China

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    摘要:

    烧结机作为烧结环节的重要设备,其轮轴健康状态对于烧结机安全生产具有重要意义。车轮摆动作为轮轴故障的典型特征,可以用来对其进行监测与预警。本文针对生产现场缺少对烧结机台车轮轴故障预警的有效手段,提出了一种基于车轮摆动角度的检测方案,并构建了基于改进 DeepLabv3 + 的烧结机台车车轮摆动的检测算法。该算法通过对比选取车轮分割性能最优的模型; 根据分割后的车轮掩膜图像,确定基准线与车轮的最小外接矩形框,利用其夹角确定车轮的摆动角度; 设定预警规则,通过车轮摆动角度与预警规则对比,对车轮摆动按照风险等级分类。试验表明,最优改进 DeepLabv3 + 模型相较于初始模型平均交并比提升 1. 7% ,模型大小减少 92. 9% ; 使用其作为分割模型对车轮摆动角度检测,角度误差在所设定区间达到 0. 6°以下; 利用设定的预警规则进行车轮摆动故障的判定,测得平均正确检出率为 96. 1% ,在此基础上设计了车轮摆动自动检测系统,可以为轮轴故障的诊断提供技术支持。

    Abstract:

    As an important equipment in the sintering process,the health state of the wheel axle of the sintering machine is of great significance to the safety production of the sintering machine. As a typical feature of axle failure,wheel oscillation can be used to monitor and warn of axle failure. In view of the lack of effective means for early warning of wheel axle failure of sintering machine in the production site,a detection scheme based on wheel swing angle is proposed,and a detection algorithm for wheel swing of sintering machine based on improved DeepLabv3 + is constructed. The algorithm selects the model with the best wheel segmentation performance by comparison. According to the segmented wheel mask image,the minimum external rectangular frame between the reference line and the wheel is determined,the swing angle of the wheel is determined by its included angle,and early warning rules are set. By comparing the wheel swing angle with the early warning rules,the wheel swing is classified according to the risk level. The experimental results show that the average intersection and union ratio of the optimally improved DeepLabv3 + model is increased by 1. 7% and the model size is reduced by 92. 9% compared with the initial model. It is used as a segmentation model to detect the swing angle of the wheel,and the angle error reaches less than 0. 6° in the set range. The average correct detection rate of 96. 1% is measured by using the set early warning rules to determine the wheel swing fault,and the automatic detection system for wheel swing is designed on this basis,which could provide technical support for the diagnosis of wheel axle fault.

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引用本文

张昊,陈波,梅佳锐,杨虎生,王月明.基于改进DeepLabv3+的烧结机车轮摆动检测算法研究[J].烧结球团,2025,50(1):31-37

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  • 收稿日期:2024-01-17
  • 最后修改日期:2024-04-07
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  • 在线发布日期: 2025-11-17
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