1,310
Views
8
CrossRef citations to date
0
Altmetric
Research Article

Microservices-based cloud-edge collaborative condition monitoring platform for smart manufacturing systems

, , , &
Pages 7492-7501 | Received 11 Jan 2022, Accepted 27 Jun 2022, Published online: 14 Jul 2022

References

  • Alaasam, Ameer B.A., Gleb Radchenko, and Andrey Tchernykh. 2019. “Stateful Stream Processing for Digital Twins: Microservice-Based Kafka Stream DSL.” SIBIRCON 2019 – International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings, 804–809. IEEE. doi:10.1109/SIBIRCON48586.2019.8958367.
  • An, Dawn, Joo-Ho Choi, and Nam Ho Kim. 2013. “Prognostics 101: A Tutorial for Particle Filter-Based Prognostics Algorithm Using Matlab.” Reliability Engineering & System Safety 115: 161–169. doi:10.1016/j.ress.2013.02.019.
  • Bernstein, David. 2014. “Containers and Cloud: From LXC to Docker to Kubernetes.” IEEE Cloud Computing 1 (3): 81–84. doi:10.1109/MCC.2014.51.
  • Bi, Zhuming, Yan Jin, Paul Maropoulos, Wen Jun Zhang, and Lihui Wang. 2021. “Internet of Things (IoT) and Big Data Analytics (BDA) for Digital Manufacturing (DM).” International Journal of Production Research. doi:10.1080/00207543.2021.1953181.
  • Cisco. 2018. “Global Cloud Index: Forecast and Methodology, 2016–2021.” Cisco. Accessed January 7, 2022. https://www.cisco.com.
  • Delaram, Jalal, Mahmoud Houshamand, Farid Ashtiani, and Omid Fatahi Valilai. 2021. “A Utility-Based Matching Mechanism for Stable and Optimal Resource Allocation in Cloud Manufacturing Platforms Using Deferred Acceptance Algorithm.” Journal of Manufacturing Systems 60: 569–584. doi:10.1016/j.jmsy.2021.07.012.
  • Dinh-Tuan, Hai, Felix Beierle, and Sandro Rodriguez Garzon. 2019. “MAIA: A Microservices-Based Architecture for Industrial Data Analytics.” In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), 23–30. IEEE. doi:10.1109/ICPHYS.2019.8780345.
  • Gao, Robert X., Lihui Wang, Moneer Helu, and Roberto Teti. 2020. “Big Data Analytics for Smart Factories of the Future.” CIRP Annals – Manufacturing Technology 69 (2): 668–692. doi:10.1016/j.cirp.2020.05.002.
  • Gao, R., L. Wang, R. Teti, D. Dornfeld, S. Kumara, M. Mori, and M. Helu. 2015. “Cloud-Enabled Prognosis for Manufacturing.” CIRP Annals – Manufacturing Technology 64 (2): 749–772. doi:10.1016/j.cirp.2015.05.011.
  • Goethals, Tom, Filip DeTurck, and Bruno Volckaert. 2020. “Extending Kubernetes Clusters to Low-Resource Edge Devices Using Virtual Kubelets.” IEEE Transactions on Cloud Computing. doi:10.1109/TCC.2020.3033807.
  • Hasan, Mahmud, and Binil Starly. 2020. “Decentralized Cloud Manufacturing-as-a-Service (CMaaS) Platform Architecture with Configurable Digital Assets.” Journal of Manufacturing Systems 56: 157–174. doi:10.1016/j.jmsy.2020.05.017.
  • Homay, Aydin, Alois Zoitl, Mario de Sousa, and Martin Wollschlaeger. 2019. “A Survey: Microservices Architecture in Advanced Manufacturing Systems.” In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 1165–1168. IEEE. doi:10.1109/INDIN41052.2019.8972079.
  • Jamshidi, Pooyan, Claus Pahl, Nabor C. Mendonca, James Lewis, and Stefan Tilkov. 2018. “Microservices: The Journey So Far and Challenges Ahead.” IEEE Software 35 (3): 24–35. doi:10.1109/MS.2018.2141039.
  • Jardine, Andrew K.S., Daming Lin, and Dragan Banjevic. 2006. “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance.” Mechanical Systems and Signal Processing 20 (7): 1483–1510. doi:10.1016/j.ymssp.2005.09.012.
  • JupyterLab image. 2022. Accessed January 7, 2022. https://github.com/jupyter/docker-stacks.
  • Kaur, Kuljeet, Sahil Garg, Georges Kaddoum, Syed Hassan Ahmed, and Mohammed Atiquzzaman. 2020. “KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud Ecosystem.” IEEE Internet of Things Journal 7 (5): 4228–4237. doi:10.1109/JIOT.2019.2939534.
  • Kubernetes. 2022. Accessed January 7, 2022. https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/.
  • Li, Ming, Yelin Fu, Qiqi Chen, and Ting Qu. 2021. “Blockchain-Enabled Digital Twin Collaboration Platform for Heterogeneous Socialized Manufacturing Resource Management.” International Journal of Production Research, 1–21. doi:10.1080/00207543.2021.1966118.
  • Li, Pin, Xiaodong Jia, Jianshe Feng, Hossein Davari, Guan Qiao, Yihchyun Hwang, and Jay Lee. 2018. “Prognosability Study of Ball Screw Degradation Using Systematic Methodology.” Mechanical Systems and Signal Processing 109: 45–57. doi:10.1016/j.ymssp.2018.02.046.
  • Li, Xiaomin, Jiafu Wan, Hong-Ning Dai, Muhammad Imran, Min Xia, and Antonio Celesti. 2019. “A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing.” IEEE Transactions on Industrial Informatics 15 (7): 4225–4234. doi:10.1109/TII.2019.2899679.
  • Liu, Lisi, Yingxue Yao, and Jianguang Li. 2021. “Service-Oriented Invisible Numerical Control Application: Architecture, Implementation, and Test.” International Journal of Production Research. doi:10.1080/00207543.2021.1896818.
  • Ren, Lei, Yuxin Liu, Xiaokang Wang, Jinhu Lu, and M. Jamal Deen. 2021. “Cloud–Edge-Based Lightweight Temporal Convolutional Networks for Remaining Useful Life Prediction in IIoT.” IEEE Internet of Things Journal 8 (16): 12578–12587. doi:10.1109/JIOT.2020.3008170.
  • Schneider, Stefan, Manuel Peuster, Kai Hannemann, Daniel Behnke, Marcel Muller, Patrick-Benjamin Bok, and Holger Karl. 2019. “‘Producing Cloud-Native’: Smart Manufacturing Use Cases on Kubernetes.” In 2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 1–2. IEEE. doi:10.1109/NFV-SDN47374.2019.9040152.
  • Si, Xiao Sheng. 2015. “An Adaptive Prognostic Approach via Nonlinear Degradation Modeling: Application to Battery Data.” IEEE Transactions on Industrial Electronics 62 (8): 5082–5096. doi:10.1109/TIE.2015.2393840.
  • Simeone, Alessandro, Alessandra Caggiano, Lev Boun, and Bin Deng. 2019. “Intelligent Cloud Manufacturing Platform for Efficient Resource Sharing in Smart Manufacturing Networks.” Procedia CIRP 79: 233–238. doi:10.1016/j.procir.2019.02.056.
  • Siqueira, Frank, and Joseph G. Davis. 2022. “Service Computing for Industry 4.0: State of the Art, Challenges, and Research Opportunities.” ACM Computing Surveys 54 (9): 1–38. doi:10.1145/3478680.
  • Tao, Fei, and Qinglin Qi. 2019. “New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 49 (1): 81–91. doi:10.1109/TSMC.2017.2723764.
  • Tao, Fei, Qinglin Qi, Ang Liu, and Andrew Kusiak. 2018a. “Data-Driven Smart Manufacturing.” Journal of Manufacturing Systems 48: 157–169. doi:10.1016/j.jmsy.2018.01.006.
  • Tao, Fei, Meng Zhang, Yushan Liu, and A. Y. C. Nee. 2018b. “Digital Twin Driven Prognostics and Health Management for Complex Equipment.” CIRP Annals 67 (1): 169–172. doi:10.1016/j.cirp.2018.04.055.
  • Thramboulidis, Kleanthis, Danai C. Vachtsevanou, and Ioanna Kontou. 2019. “CPuS-IoT: A Cyber-Physical Microservice and IoT-Based Framework for Manufacturing Assembly Systems.” Annual Reviews in Control 47: 237–248. doi:10.1016/j.arcontrol.2019.03.005.
  • Toka, Laszlo, Gergely Dobreff, Balazs Fodor, and Balazs Sonkoly. 2021. “Machine Learning-Based Scaling Management for Kubernetes Edge Clusters.” IEEE Transactions on Network and Service Management 18 (1): 958–972. doi:10.1109/TNSM.2021.3052837.
  • Wang, Yuanbin, Yuan Lin, Ray Y. Zhong, and Xun Xu. 2019. “IoT-Enabled Cloud-Based Additive Manufacturing Platform to Support Rapid Product Development.” International Journal of Production Research 57 (12): 3975–3991. doi:10.1080/00207543.2018.1516905.
  • Wang, Yimeng, Cong Zhao, Shusen Yang, Xuebin Ren, Luhui Wang, Peng Zhao, and Xinyu Yang. 2021. “MPCSM: Microservice Placement for Edge-Cloud Collaborative Smart Manufacturing.” IEEE Transactions on Industrial Informatics 17 (9): 5898–5908. doi:10.1109/TII.2020.3036406.
  • Wu, Dazhong, Connor Jennings, Janis Terpenny, Soundar Kumara, and Robert X. Gao. 2018. “Cloud-Based Parallel Machine Learning for Tool Wear Prediction.” Journal of Manufacturing Science and Engineering, Transactions of the ASME. doi:10.1115/1.4038002.
  • Xu, Xun. 2012. “From Cloud Computing to Cloud Manufacturing.” Robotics and Computer-Integrated Manufacturing 28 (1): 75–86. doi:10.1016/j.rcim.2011.07.002.
  • Yang, Hanbo, Gedong Jiang, Zheng Sun, Zhuoni Zhang, Fei Zhao, Tao Tao, and Xuesong Mei. 2021a. “Remaining Useful Life Prediction of Ball Screw Using Precision Indicator.” IEEE Transactions on Instrumentation and Measurement. doi:10.1109/TIM.2021.3087803.
  • Yang, Hanbo, Xuesong Mei, Gedong Jiang, Tao Tao, Zheng Sun, and Fei Zhao. 2022. “Remaining Useful Life Prediction of Ball Screw Under Time-Varying Conditions With Limited Data.” IEEE/ASME Transactions on Mechatronics, 1–10. doi:10.1109/TMECH.2022.3144351.
  • Yang, Hanbo, Zheng Sun, Gedong Jiang, Fei Zhao, Xufeng Lu, and Xuesong Mei. 2021b. “Cloud-Manufacturing-Based Condition Monitoring Platform With 5G and Standard Information Model.” IEEE Internet of Things Journal 8 (8): 6940–6948. doi:10.1109/JIOT.2020.3036870.
  • Yang, Bo, Shilong Wang, Shi Li, and Tianguo Jin. 2020. “A Robust Service Composition and Optimal Selection Method for Cloud Manufacturing.” International Journal of Production Research. doi:10.1080/00207543.2020.1852481.
  • Zheng, Ting, Marco Ardolino, Andrea Bacchetti, and Marco Perona. 2021. “The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review.” International Journal of Production Research 59 (6): 1922–1954. doi:10.1080/00207543.2020.1824085.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.