吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 439-449.doi: 10.13229/j.cnki.jdxbgxb20211230

• 车辆工程·机械工程 • 上一篇    

基于信息物理融合和数字孪生的可靠性分析

宋林1,2(),王立平2,3,吴军3(),关立文3,刘知贵2   

  1. 1.攀枝花学院 智能制造学院，四川 攀枝花 617000
    2.西南科技大学 信息工程学院，四川 绵阳 621000
    3.清华大学 机械工程系，北京 100084
  • 收稿日期:2021-11-17 出版日期:2022-02-01 发布日期:2022-02-17
  • 通讯作者: 吴军 E-mail:songlin606060@163.com;jhwu@mail.tsinghua.edu.cn
  • 作者简介:宋林（1991-），男，博士研究生.研究方向：信息物理融合建模，可靠性分析.E-mail：songlin606060@163.com
  • 基金资助:
    国家重点研发计划项目(2018YFB1703502)

Reliability analysis based on cyber⁃physical system and digital twin

Lin SONG1,2(),Li-ping WANG2,3,Jun WU3(),Li-wen GUAN3,Zhi-gui LIU2   

  1. 1.College of Intelligent Manufacturing，Panzhihua University，Panzhihua 617000，China
    2.College of Information Engineering，Southwest University of Science and Technology，Mianyang 621000，China
    3.Department of Mechanical Engineering，Tsinghua University，Beijing 100084，China
  • Received:2021-11-17 Online:2022-02-01 Published:2022-02-17
  • Contact: Jun WU E-mail:songlin606060@163.com;jhwu@mail.tsinghua.edu.cn

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摘要：

针对实际应用中缺乏统一集成的用于数控装备可靠性分析的信息物理融合系统框架和算法实现，本文提出了一种基于数字孪生的方法，研究了具体的框架搭建和算法实现。通过数据采集、数据处理、数字孪生模型训练和评估、模型调试和优化、模型在线部署、可靠性分析、预测性维护7步序列化的工作流程实现了从物理层到信息层再返回物理层的闭环控制。通过数控装备主轴回转误差预测可靠性实验验证了该信息物理融合框架的可行性和有效性，该框架和算法能够对数控装备进行可靠性分析，有助于支持更有效和科学的预测性维护。

关键词: 机械制造及其自动化, 数字孪生, 数控装备, 可靠性分析, 深度学习, 信息物理融合

Abstract:

In view of the lack of unified integrated system framework and algorithm implementation for reliability analysis of CNC equipment in practical application， in this paper， a cyber-physical system based on digital twin is proposed， and the specific framework and algorithm implementation are studied. The closed-loop control from the physical layer to the cyber layer and back to the physical layer can be realized through the 7-step workflow of data acquisition， data processing， digital twin model training and evaluation， model debugging and optimization， model online deployment， reliability analysis， predictive maintenance. The feasibility and effectiveness of the cyber-physical system framework were verified by the reliability experiment of spindle rotation error prediction of CNC equipment. This method can analyze the reliability of CNC equipment， and is helpful to support more effective and scientific predictive maintenance.

Key words: mechanical manufacturing and automation, digital twin, CNC equipment, reliability analysis, deep learning, cyber-physical system

中图分类号: 

  • TH161

图1

基于DT的数控装备可靠性分析CPS框架图"

图2

三种CNN结构和特征尺寸图"

图3

MSCNN结构和特征尺寸图"

表1

主轴可靠性实验台主要器件"

器件型号
主轴CTB40D
振动传感器PCB 256A14
力传感器DYMH-104
回转误差仪Lion
数据采集与控制单元NI PXIe-1082
气动加载装置AirTac-100

表2

实验结果"

数据归一化信号处理平均测试精度±标准差/%
MSCNNVGG16CNNLeNet
Z-scoreSTFT90.87±0.3989.45±0.7389.39±0.6785.25±0.95
WPD90.24±0.3987.46±0.6889.06±0.4380.14±1.72
DMT87.42±1.4781.58±1.1379.69±2.1477.12±1.68
［-1~1］STFT89.83±0.2988.08±0.9286.52±0.6678.09±2.50
WPD88.79±0.4585.35±0.8887.24±0.7673.65±1.98
DMT84.22±1.5077.56±1.7176.96±1.8870.36±1.58
Min-MaxSTFT90.76±0.2490.11±0.6188.51±0.7587.00±0.62
WPD89.94±0.4487.51±0.6288.56±0.4583.58±0.63
DMT83.09±1.7077.63±0.9376.42±0.9654.87±1.83

图4

不同深度学习方法的结果"

图5

不同数据归一化方法的结果"

图6

不同信号处理方法的结果"

表3

不同学习率的实验结果"

学习率精度±标准差/%
Z-score［-1~1］Min-Max
0.010081.03±10.9479.79±8.0175.82±10.20
0.001090.87±0.3989.83±0.2990.76±0.24
0.000188.59±0.4487.23±1.1487.95±0.99

图7

STFT+ Z-score的混沌矩阵"

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