Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (2): 439-449.doi: 10.13229/j.cnki.jdxbgxb20211230

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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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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

CLC Number: 

  • TH161

Fig.1

Reliability analysis CPS framework of CNC equipment based on DT"

Fig.2

Three categories CNN structure and feature size"

Fig.3

MSCNN structure and feature size"

Table 1

Key components of spindle reliability test bench"

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

Table 2

Experimental results"

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

Fig.4

Results of deep learning"

Fig.5

Results of data normalization"

Fig.6

Results of signal processing"

Table 3

Experimental results of different learning rates"

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

Fig.7

Confusion matrix of STFT+Z-score"

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