Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors

Liu Manxian; Xu Zijia; Shen Xujia; Li Xiaodong; Zhang Zhijun; Yang Yi

doi:10.16578/j.issn.1004.2539.2023.01.021

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Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors

Development·Application | 更新时间：2023-06-07

- Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors
- Journal of Mechanical Transmission Vol. 47, Issue 1, Pages: 147-154(2023)
- 作者机构：
  
  1.中国科学院自动化研究所，北京 100190
  2.哈尔滨理工大学计算机科学与技术学院，黑龙江哈尔滨 150080
  3.北京卫星制造厂有限公司，北京 100094
  4.南京天祥智能设备科技有限公司，江苏南京 211300
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.01.021
  CLC：
- Published：15 January 2023，
  
  Received：20 December 2021，
  
  Revised：04 July 2022，
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刘漫贤,许梓嘉,申绪佳等.输送用模锻易拆链磨损预测及可视化辅助决策[J].机械传动,2023,47(01):147-154.

Liu Manxian,Xu Zijia,Shen Xujia,et al.Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors[J].Journal of Mechanical Transmission,2023,47(01):147-154.
刘漫贤,许梓嘉,申绪佳等.输送用模锻易拆链磨损预测及可视化辅助决策[J].机械传动,2023,47(01):147-154. DOI： 10.16578/j.issn.1004.2539.2023.01.021.

Liu Manxian,Xu Zijia,Shen Xujia,et al.Wear Prediction and Visual Decision Support of Drop Forged Rivetless Chain for Conveyors[J].Journal of Mechanical Transmission,2023,47(01):147-154. DOI： 10.16578/j.issn.1004.2539.2023.01.021.

摘要

针对输送用模锻易拆链存在的磨损状况难以准确检测、预测和检修用时长等难题，开展了基于灰色模型和可视化辅助决策的磨损寿命预测和主动检修技术研究。采用基于机器视觉的磨损检测装置获取磨损数据，通过定义和分析磨损关键参数对磨损数据进行清洗，基于灰色模型建立了磨损预测模型；基于预测数据、历史数据及运行工况等建立辅助决策模型，通过综合评估后输出最优检修方案，并基于可视化仿真技术立体、动态地呈现磨损状况，快速精确定位磨损链条。实验结果表明，磨损寿命预测模型的拟合精度较高，磨损预警误报率和漏报率较低，可满足磨损寿命预测要求。通过磨损预测及可视化辅助决策技术，可有效地提高检修效率，减少生产线受迫停运的风险。

Abstract

In order to accurately detect

predict and maintain the wear condition of drop forged rivetless chain for conveyors

the methods of wear prediction and active maintenance are developed based on gray model and visual decision support. The wear data are acquired by wear detection device based on machine vision. The obtained data are cleaned by defining and analyzing key parameters of wear condition. A wear prediction model is built based on the gray model. A decision-making support model is constructed that can obtain the optimal plan of maintenance by comprehensively evaluating the wear condition with the forecast data

historical data

and operating condition data. The wear condition can be presented in a three-dimensional and dynamic manner to quickly and accurately locate the wear chain by a visual simulation method. The experiment results show that the wear life prediction model has high fitting accuracy

low false alarm rates

and low missed detection rates of wear early-warning

which can satisfy the requirements of wear life prediction. Through the proposed wear prediction model and visual decision support method

the maintenance efficiency is significantly improved

and the risk of forced shutdown of the production line is reduced.

关键词

模锻易拆链可视化辅助决策磨损预测灰色模型机器视觉

Keywords

Drop forged rivetless chainVisual decision supportWear predictionGray modelMachine vision

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