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"

1 Chen B, Liu Y, Zhang C, et al. Time series data for equipment reliability analysis with deep learning[J]. IEEE Access, 2020, 8(99): 105484-105493.
2 Zuo Y, Wang H, Wu G, et al. Research on remote state monitoring and intelligent maintenance system of CNC machine tools[J]. The Journal of Engineering, 2019, 23: 8671-8675.
3 Kang Z, Catal C, Tekinerdogan B. Machine learning applications in production lines: a systematic literature review[J]. Computers & Industrial Engineering, 2020, 149: 106773.
4 Liu P L, Du Z C, Li H M, et al. Thermal error modeling based on BiLSTM deep learning for CNC machine tool[J]. Advances in Manufacturing, 2021(9): 235-249.
5 Chen Y, Bian R, Ding W Z, et al. A Fault Diagnosis Method of CNC Machine Tool Spindle Based on Deep Transfer Learning[C]∥Computer Information Analytics and Intelligent Systems, Kuala Lumpur, 2019: 136-140.
6 Duan C Q, Deng C, Li N. Reliability assessment for CNC equipment based on degradation data[J]. The International Journal of Advanced Manufacturing Technology, 2018, 100(2): 421- 434.
7 Marani M, Zeinali M, Songmene V, et al. Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling[J]. Measurement, 2021, 177: 109329.
8 于向军, 槐元辉, 姚宗伟, 等. 工程车辆无人驾驶关键技术[J]. 吉林大学学报: 工学版, 2021, 51(4): 1153-1168.
Yu Xiang-jun, Huai Yuan-hui, Yao Zong-wei, et al. Key technologies in autonomous vehicle for engineering[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1153-1168.
9 He B, Liu L, Zhang D. Digital twin driven remaining useful life prediction for gear performance degradation: a review[J]. Journal of Computing and Information Science in Engineering, 2021, 21(3): 030801.
10 Wang J, Ye L, Gao R, et al. Digital twin for rotating machinery fault diagnosis in smart manufacturing[J]. International Journal of Production Research, 2019, 57: 3920-3934.
11 Luo W, Hu T, Zhang C, et al. Digital twin for CNC machine tool: modeling and using strategy[J]. Journal of Ambient Intelligence and Humanized Computing, 2018, 10(3): 1-12.
12 Luo W, Hu T, Ye Y, et al. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin[J]. Robotics and Computer-Integrated Manufacturing, 2020, 65: 101974.
13 Tao F, Zhang M, Liu Y, et al. Digital twin driven prognostics and health management for complex equipment[J]. Cirp Annals, 2018, 67: 169-172.
14 Tong M, Jing X Y, Zheng Y, et al. A survey on machine learning for data fusion[J]. Information Fusion, 2020, 57(1): 115-129.
15 Zhao Z, Li T, Wu J, et al. Deep learning algorithms for rotating machinery intelligent diagnosis: an open source benchmark study[J]. ISA Transactions, 2020, 107: 224-255.
16 金立生, 郭柏苍, 王芳荣, 等.基于改进YOLOv3的车辆前方动态多目标检测算法[J]. 吉林大学学报: 工学版, 2021, 51(4): 1427-1436.
Jin Li-sheng, Guo Bai-cang, Wang Fang-rong, et al. Dynamic multiple object detection algorithm for vehicle forward based on improved YOLOv3[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1427-1436.
17 王立平, 张彬彬, 吴军. 基于最小二乘法的电主轴回转精度评价简[J]. 制造技术与机床, 2018(2): 54-60.
Wang Li-ping, Zhang Bin-bin, Wu Jun. Rotation accuracy evaluation of electric spindle based on least square method[J]. Manufacturing Technology & Machine Tool, 2018(2): 54-60.
18 王立平, 赵钦志, 张彬彬. 加工中心高速电主轴综合精度分析[J]. 清华大学学报: 自然科学版, 2018, 58(8): 746-751.
Wang Li-ping, Zhao Qin-zhi,Zhang Bin-bin. Accuracy of an electric spindle[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(8):746-751.
19 Anandan K P, Ozdoganlar O B. A multi orientation error separation technique for spindle metrology of miniature ultra-high-speed spindles[J]. Precision Engineering, 2016, 43(1): 119-131.
20 Liang J, Wang L, Wu J, et al. Prediction of spindle rotation error through vibration signal based on Bi-LSTM classification network[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1043(4): 42011-42033.
21 陈代伟, 吴军, 张彬彬, 等. 基于S试件的加工中心电主轴载荷谱编制[J]. 清华大学学报: 自然科学版, 2018, 58(12): 1107-1114.
Chen Dai-wei, Wu Jun, Zhang Bin-bin, et al. Load spectrum compilation for machining center spindles based on S-shaped specimens[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(12): 1107-1114.
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