Research on Data-driven Performance Degradation Mechanism Modelling and Predictive Control Method for Discrete Manufacturing System

doi:10.3901/JME.2024.16.400

Abstract

Abstract: The active sensing and optimal control of abnormal events are the core issues to ensure the reliable operation of discrete manufacturing system. For a discrete manufacturing system, many uncertain factors exist in the production system, the degradation patterns of production performance are hard to describe, and the cloud-based data fusion methods consume a long time, as a result, the detection of abnormal events often delays and the optimal decisions are hard to find. To meet these requirements, it is provided a data-driven production performance degradation mechanism and predictive control method for discrete manufacturing system. Firstly, the industrial Internet of Things and Cyber Physical System technologies are combined to establish a cloud-edge cooperation environment and extract the key production performance information. Secondly, the degradation mechanism of production performance is modelled based on the fusion of spatial-temporal manufacturing data. Thirdly, the operational knowledge and predicted performance of manufacturing system will be combined to present an exception early-warning and proactively optimal control method. Then, the capacity of cloud-edge cooperation, early-warning of production exceptions and predictive and optimal control of smart discrete manufacturing system can be achieved. The proposed strategy, method and model provide the important support and technical reference for the predictive control of next generation smart factory.

Key words: smart manufacturing, self-adaptive optimization, discrete manufacturing system, performance degradation, predictive control

CLC Number:

TP39
TP301

WANG Wenbo, ZHANG Yingfeng, GU Jinan, ZHANG Geng, WAN Yan. Research on Data-driven Performance Degradation Mechanism Modelling and Predictive Control Method for Discrete Manufacturing System[J]. Journal of Mechanical Engineering, 2024, 60(16): 400-411.

References

[1] 任杉,张映锋,黄彬彬. 生命周期大数据驱动的复杂产品智能制造服务新模式研究[J]. 机械工程学报,2018,54(22):194-203. REN Shan,ZHANG Yingfeng,HUANG Binbin. New pattern of lifecycle big-data-driven smart manufacturing service for complex product[J]. Journal of Mechanical Engineering,2018,54(22):194-203.
[2] GUO Z,ZHANG Y,ZHAO X,et al. CPS-based self-adaptive collaborative control for smart production-logistics systems[J]. IEEE Transactions on Cybernetics,2020,51(1):188-198.
[3] 李浩,王昊琪,程颖,等. 数据驱动的复杂产品智能服务技术与应用[J]. 中国机械工程,2020,31(7):757-772. LI Hao,WANG Haoqi,CHENG Ying,et al. Technology and application of data-driven intelligent services for complex products[J]. China Mechanical Engineering,2020,31(7),757-772.
[4] WANG W,ZHANG Y,GU J,et al. A proactive manufacturing resources assignment method based on production performance prediction for the smart factory[J]. IEEE Transactions on Industrial Informatics,2022,18(1):46-55.
[5] ZHANG Y,ZHANG G,WANG J,et al. Real-time information capturing and integration framework of the internet of manufacturing things[J]. International Journal of Computer Integrated Manufacturing,2015,28(8):811-822.
[6] WANG H,GONG Q,WANG S. Information processing structures and decision-making delays in MRP and JIT[J]. International Journal of Production Economics,2017,188:41-49.
[7] LI T,SHI T,TANG Z,et al. Real-time tool wear monitoring using thin-film thermocouple[J]. Journal of Materials Processing Technology,2021,288(116901):1-8.
[8] ZHANG J,DENG C Y,ZHENG P,et al. Development of an edge computing-based cyber-physical machine tool[J]. Robotics and Computer-Integrated Manufacturing,2021,67(102042):1-12.
[9] SAEZ M,MATURANA F,BARTON K. Real-time manufacturing machine and system performance monitoring using internet of things[J]. IEEE Transactions on Automation Science and Engineering,2018,15(4):1735-1748.
[10] ZOU J,CHANG Q,ARINEZ J,et al. Production performance prognostics through model-based analytical method and recent-weighted stochastic approximation method[J]. Journal of Manufacturing Systems,2018,47:107-114.
[11] HU H,LIU Y. Supervisor synthesis and performance improvements for automated manufacturing systems using petri nets[J]. IEEE Transactions on Industrial Informatics,2015,11(2):450-458.
[12] JIA Z,ZHAO K,ZHANG Y,et al. Real-time performance evaluation and improvement of assembly systems with Bernoulli machines and finite production runs[J]. International Journal of Production Research,2019,57(18):5749-5766.
[13] 卫炳坤,王庆锋,刘家赫,等. 基于动态长短期记忆网络的设备性能退化预测方法[J]. 北京化工大学学报(自然科学版),2020,47(6):92-99. WEI Bingkun,WANG Qingfeng,LIU Jiahe,et al. An equipment performance degradation prediction method based on a dynamic long-short-term memory network[J]. Journal of Beijing University of Chemical Technology (Natural Science),2020,47(6):92-99.
[14] WANG Y,XU J,SUN Y. Tool orientation adjustment for improving the kinematics performance of 5-axis ball-end machining via CPM method[J]. Robotics and Computer- Integrated Manufacturing,2021,68(102070):1-12.
[15] XU L,HUANG C,LI C,et al. An improved case-based reasoning method and its application in estimation of surface quality toward intelligent machining[J]. Journal of Intelligent Manufacturing,2021,32(1):313-327.
[16] 陈超逸,鲁娟,陈楷,等. 车削表面粗糙度解析模型与DDQN-SVR预测模型研究[J]. 机械工程学报,2021,57(13):262-272. CHEN Chaoyi,LU Juan,CHEN Kai,et al. Research on analytical model and DDQN-SVR prediction model of turning surface roughness[J]. Journal of Mechanical Engineering,2021,57(13):262-272.
[17] YE Z,YANG H,CAI Z,et al. Performance evaluation of serial-parallel manufacturing systems based on the impact of heterogeneous feedstocks on machine degradation[J]. Reliability Engineering & System Safety,2021,207(107319):1-11.
[18] 丁华,杨亮亮,杨兆建,等. 数字孪生与深度学习融合驱动的采煤机健康状态预测[J]. 中国机械工程,2020,31(7):815-823. DING Hua,YANG Liangliang,YANG Zhaojian,et al. Health prediction of shearers driven by digital twin and deep learning[J]. China Mechanical Engineering. 2020,31(7):815-823.
[19] WANG J,ZHANG J,WANG X. A data driven cycle time prediction with feature selection in a semiconductor wafer fabrication system[J]. IEEE Transactions on Semiconductor Manufacturing,2018,31(1):173-182.
[20] 王庆锋,卫炳坤,刘家赫,等. 一种数据驱动的旋转机械早期故障检测模型构建和应用研究[J]. 机械工程学报,2020,56(16):22-32. WANG Qingfeng,WEI Bingkun,LIU Jiahe,et al. Research on construction and application of data-driven incipient fault detection model for rotating machinery[J]. Journal of Mechanical Engineering,2020,56(16):22-32.
[21] 刘君强,谢吉伟. 基于性能退化数据融合的航空发动机剩余寿命预测方法[J]. 武汉理工大学学报,2020,44(6):994-998,1003. LIU Junqiang,XIE Jiwei. Remaining useful life prediction method of aero engine based on data-fusion of performance degradation[J]. Journal of Wuhan University of Technology,2020,44(6):994-998,1003.
[22] 李乃鹏,蔡潇,雷亚国,等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报,2021,57(20):29-37,46. LI Naipeng,CAI Xiao,LEI Yaguo,et al. A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery[J]. Journal of Mechanical Engineering,2021,57(20):29-37,46.
[23] HE Y,GU C,CHEN Z,et al. Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis[J],International Journal of Production Research,2017,55(19):5841-5862.
[24] WAN J,TANG S,LI D,et al. A manufacturing big data solution for active preventive maintenance[J]. IEEE Transactions on Industrial Informatics,2017,13(4):2039-2047.
[25] ZHAO Z,WANG S,WANG Z,et al. Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment:A case study in five axis machining[J]. Journal of Intelligent Manufacturing,2022,33:943-952.
[26] ADAMSON G,WANG L,MOORE P. Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems[J]. Journal of Manufacturing Systems,2017,43:305-315.
[27] BILBERG A,MALIK A. Digital twin driven human robot collaborative assembly[J]. CIRP Annals-Manufacturing Technology,2019,68(1):499-502.
[28] 张映锋,郭振刚,钱成,等. 基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究[J]. 机械工程学报,2018,54(16):1-10. ZHANG Yingfeng,GUO Zhengang,QIAN Chen,et al. Investigation on process aware based intelligent modeling of bottom layer manufacturing resources and self-adaptive collaborative optimization methodology[J]. Journal of Mechanical Engineering,2018,54(16):1-10.