数字孪生与神经网络融合驱动的烧成系统故障预警研究

罗昌亮; 毛娅; 陈作炳; 邓宇

doi:10.13433/j.cnki.1003-8728.20240016

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数字孪生与神经网络融合驱动的烧成系统故障预警研究

更新时间：2026-04-23

- 数字孪生与神经网络融合驱动的烧成系统故障预警研究
- Study on Failure Warning of Firing System Driven by Fusion of Digital Twin and Neural Network
- 机械科学与技术 2026年45卷第2期页码：253-260
- 作者机构：
  
  武汉理工大学机电工程学院,武汉,430070
- 作者简介：
  
  [ "罗昌亮，硕士研究生，" ]
  [ "毛娅，副教授，博士," ]
- 基金信息：
- DOI：10.13433/j.cnki.1003-8728.20240016
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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罗昌亮, 毛娅, 陈作炳, 等. 数字孪生与神经网络融合驱动的烧成系统故障预警研究[J]. 机械科学与技术, 2026,45(2):253-260.

罗昌亮, 毛娅, 陈作炳, et al. Study on Failure Warning of Firing System Driven by Fusion of Digital Twin and Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2026, 45(2): 253-260.
罗昌亮, 毛娅, 陈作炳, 等. 数字孪生与神经网络融合驱动的烧成系统故障预警研究[J]. 机械科学与技术, 2026,45(2):253-260. DOI： 10.13433/j.cnki.1003-8728.20240016.

罗昌亮, 毛娅, 陈作炳, et al. Study on Failure Warning of Firing System Driven by Fusion of Digital Twin and Neural Network[J]. Mechanical Science and Technology for Aerospace Engineering, 2026, 45(2): 253-260. DOI： 10.13433/j.cnki.1003-8728.20240016.

摘要

针对烧成系统动态耦合关系下的工艺故障诊断预测困难等问题，融合数字孪生高度刻画虚实交互特性和神经网络强大的数据分析能力，提出一种数字孪生与神经网络融合驱动的烧成系统故障诊断预警方法。基于Unity3D构建对烧成系统物理车间高度刻画的数字孪生车间；建立基于ELM神经网络的烧成系统故障诊断模型，实现实时监测数据驱动下的烧成系统设备的故障预测，并在虚拟车间中以面板形式对故障做出预警，为烧成系统的故障诊断预测问题提供新思路，对水泥工业的数字化转型具有重要意义。

Abstract

Aiming at the difficulty of process fault diagnosis and prediction under the dynamic coupling relationship of the firing system

a digital twin and neural network fusion-driven fault diagnosis and warning method of firing system is proposed by integrating the digital twin with the powerful data analyzing capability of neural network

which is highly characterized by using the interaction between the reality. Based on Unity3D

a digital twin workshop that highly portrays the physical workshop of the firing system is constructed; a failure diagnosis model for firing system based on the extreme learning machine neural network is established

the failure prediction of the firing system equipment driven by real-time monitoring data is realized

and a warning of the failure in the form of a panel in the virtual workshop is made

which provides a new way in thinking for the failure diagnosis and prediction of the firing system

and has important significance for the digital transformation of the cement industry.

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