Digital Twin modelling for automation, maintenance and monitoring in Industry 4.0 smart factory

Funders: UK India Education and Research Initiative (UKIERI), UK and the Department of Science and Technology (DST), India.

Duration: 2019-2021

Project Partners: London Digital Twin Research Centre (Middlesex University), and Indian Institute of Information Technology (IIIT) Sri City

Industrial Partners: SPL, Festo Didatic, Siemens

Project Lead: Prof. Huan X. Nguyen (UK Lead) and Dr. Hrishikesh V Raman (Indian Lead)

Project Team: Dr Ramona Trestian (Co-I), Prof. Mehmet Karamanoglu, and Prof. Balbir Barn

Goals and Objectives

  1. Produce state of the art digital twin model to work with Siemens/Festo Industry 4.0 Cyber physical facility for thorough evaluation, debugging and optimization of applications
  2. Use proposed digital twin to suggest and counteract delay inducing elements in safety, preventive maintenance and regulatory systems in Industry 4.0
  3. Digitizing manufacturing processes in industry 4.0 for optimal efficiency, including detecting and solving physical issues faster, predicting outcomes to a much higher degree of accuracy, scheduling activities in the most efficient and cost-conscious way

Brief Description of the Project

The rapid advancements in manufacturing technologies and industry transformation in 4th Industrial Revolution requires more sophisticated tools to enable high productivity, lower running costs, product quality improvement, minimized maintenance and shutdown. In Industry 4.0, fully automated smart industrial infrastructure relies on low latency feedback networks, high efficiency distributed control systems, fool-proof emergency and safety systems, energy efficient and self-sustaining processes and supportive digital technologies.

The existing industrial systems are highly complex and require several processes to operate simultaneously to achieve the desired objectives. To ensure efficient operations within industrial processes, human intelligence, intervention and feedback is widely used. To enable truly self-reliant and autonomous industries, the developments are on the way. One major hurdle in achieving fully autonomous industries is lack of software-based counterparts to support vigorous testing.

This project targets implementation of digital counterpart (a Digital Twin model) of Industry 4.0 to replicate its functionalities, data, communications, feedback, emergency and safety aspects. The proposed digital twin for industry 4.0 will not only offer a digitised replication of functionalities but will also enable development towards self-correcting smart process control facility. The digital twin will also facilitate debugging, testing and reforming processes. It is expected that the developments in the project will provide solutions for some of the most critical aspects of the present-day industries. The developments in this project will be cross-validated and vigorously tested in state of the art Siemens/Festo cyber factory facility installed at Middlesex University (MDX), which acts as the physical twin in the project.

The key research question that will be addressed is how intelligently digital twin can predict the chain of events triggered as a consequence of certain variations in some processes, within the hundred plus manufacturing industries in and around Sricity/Andhra-Pradesh.

Scientific & Technical Details

Industry 4.0 aims to offer next generation of industrial automation which emphasises on interconnected and decentralized intelligent systems, capable of self-sustaining. However, the complexity of smart industrial processes is unfathomable, given the interjection of countless smart processes, which need to work seamlessly perfect to achieve the desired outcomes. For such interconnected systems, the impact of changes in one process is hard to predict.

The use of digital twin encompasses the functionality and interconnection of different processes within the industry and bears the potential to replicate interlinked complex processes in digital domain. This can provide a framework to investigate experimental setup in the simulations with more confidence. It also offers a platform to evaluate system limitations and impact of malfunction in one process on the others. The digital model of industry 4.0 will provide limitless opportunities to observe the impact of failure in one small block and how it will impact the entire setup. It will also enable the development of backup solutions to deal with the arising situation. Notably, the IoT and Analytics are required for real-time data collection, analysis and decision making which are crucial for the proper operation of the Cyber Physical System (CPS). The interaction of the IoT-based smart objects within the CPS will generate large amounts of data needs to be processed for extracting valuable and timely information.

This project targets implementation of digital counterpart of industry 4.0 to replicate its functionalities, data, communications, feedback, emergency and safety aspects. The project will develop digital twin to mirror the smart cyber factory facility at Middlesex University supplied by Festo/Siemens which comprises a comprehensive six-station table top unit (two production cells of three stations), as well as two bridging stations that enable an Automated Guided Vehicle (AGV) to deliver the logistics/transport between the cells. The validation for the Digital Twin will focus on the following aspects: i) energy monitoring; ii) tracking components and goods by means of tags which transmit a radio signal using Radio Frequency identification (RFID); iii) digital maintenance; iv) augmented reality of a real-world manufacturing process; v) direct communication among the objects using near field communication (i.e., objects equipped with a chip to exchange information directly); and vi) manufacturing execution system. The primary objectives are to develop a self-correcting smart process control facility where the digital twin can extend the debugging, testing and reforming processes before physical implementation. It is expected that the developments in the project will provide solutions for some of the most critical aspects of the present-day industries. The project will also aim to minimize the sensing, communications and processing delays for such applications. It will also target regulatory control applications within industry 4.0 to improve the overall efficiency of the plant/factory. Since the effective operation of regulatory control in industries require feedback response within a fixed time window for optimal process efficiency, therefore, the digital twin will serve as a digital alternate to predict any expected variations in regulatory control systems’ delays.

Results and Outputs

  • A video about the project: here
  • Presentation of summary of the project: here (full workshop version is here)
  • Some demo videos of the developed DT: here, here, and here
  • Organised 4 dissemination workshops (two in the UK + two in India):  Examples are here, here, here and here 
  • Trained 16 students and research assistants
  • Two follow-up projects secured:

    1. “Towards Resilient Automation in Industry 4.0 for Future Engineers – Specialisation Programme”, (2022), UK-India Going Global Partnerships Grant, British Council
    2. “Developing Connected Curriculum on Digital Twin for Health System Resilience”(2022-2024), Going Global Partnerships – Indonesia
  • 14 publications:
    1. Mihai, M. Yaqoob, D.V. Hung, W. Davis, P. Towakel, M. Raza, M. Karamanoglu, B. Barn, D. Shetve, R. Prasad, H. Venkataraman, R. Trestian, and H. X. Nguyen, “Digital twins: A survey on enabling technologies, challenges, trends and future prospects,” IEEE Communications Surveys and Tutorials, vol. 24, No. 4, pp. 2255-2291, 4th Quarter, 2022.
    2. M. Hoang, S. Dinh-Van, B. Barn, R. Trestian and H. X. Nguyen, “RIS-Aided Smart Manufacturing: Information Transmission and Machine Health Monitoring,” in IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22930-22943, 15 Nov.15, 2022, doi: 10.1109/JIOT.2022.3187189.
    3. Dinh-Van, T. M. Hoang, R. Trestian and H. X. Nguyen, “Unsupervised Deep Learning-based Reconfigurable Intelligent Surface Aided Broadcasting Communications in Industrial IoTs,” in IEEE Internet of Things Journal, vol. 9, no. 19, pp. 19515-19528, 1 Oct.1, 2022, doi: 10.1109/JIOT.2022.3169276.
    4. V. Dang, M. Tatipamula and H. X. Nguyen, “Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning,” in IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 3820-3830, June 2022, doi: 10.1109/TII.2021.3115119.
    5. Niu, Z. Chu, F. Zhou, C. Pan, D. W. K. Ng and H. X. Nguyen, “Double Intelligent Reflecting Surface-Assisted Multi-User MIMO Mmwave Systems with Hybrid Precoding,” in IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1575-1587, Feb. 2022, doi: 10.1109/TVT.2021.3131514.
    6. X. Nguyen, R. Trestian, D. To and M. Tatipamula, “Digital Twin for 5G and Beyond,” in IEEE Communications Magazine, vol. 59, no. 2, pp. 10-15, Feb. 2021, doi: 10.1109/MCOM.001.2000343.
    7. K. Ali et al., “Review and Implementation of Resilient Public Safety Networks: 5G, IoT, and Emerging Technologies,” in IEEE Network, vol. 35, no. 2, pp. 18-25, March/April 2021, doi: 10.1109/MNET.011.2000418.
    8. Q. -T. Vien, “On the Cooperative Relaying Strategies for Multi-Core Wireless Network-on-Chip,” in IEEE Access, vol. 9, pp. 9572-9583, 2021, doi: 10.1109/ACCESS.2021.3049770.
    9. K. Ali, H. X. Nguyen, Q. Vien, P. Shah and M. Raza, “Deployment of Drone-Based Small Cells for Public Safety Communication System,” in IEEE Systems Journal, vol. 14, no. 2, pp. 2882-2891, June 2020, doi: 10.1109/JSYST.2019.2959668.
    10. D. Shetve, I. Raju, R. V. Prasad, R. Trestian, H. X. Nguyen and H. Venkataraman, “Adaptive N-Step Technique for Real-Time Anomaly Detection in Smart Manufacturing,” 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), Coventry, United Kingdom, 2022, pp. 01-06, doi: 10.1109/ICPS51978.2022.9816882.
    11. Davis, M. Yaqoob, S. Mihai, D. V. Hung, R. Trestian, M. Karamanoglu, B. Barn and H. X. Nguyen, “An Innovative Blockchain-based Traceability Framework for Industry 4.0 Cyber-Physical Factory,” in Proc. 11th International Conference on Industrial Technology and Management (ICITM2022).
    12. Mihai, W. Davis, H. V. Dang, R. Trestian, M. Karamanoglu, B. Barn, R. V. Prasad, H. Venkataraman, and H. X. Nguyen, “A Digital Twin Framework for Predictive Maintenance in Industry 4.0,” 2020 International Conference on High Performance Computing & Simulation (HPCS 2020), Dec. 2020.
    13. M. Raza, P. M. Kumar, H. Dang-Viet, W. Davis, H. X. Nguyen, and R. Trestian, “A Digital Twin framework for industry 4.0 enabling next-gen manufacturing,” 9th Int. Conf. on Industrial Technology and Management (ICITM 2020), Feb. 2020, Oxford, UK.
    14. Dattaprasad S. Shetve, Raja V. Prasad, Ramona Trestian, Huan X Nguyen and Hrishikesh Venkataraman, “CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing,” in  The 2020 Fourth International Conference on Computing and Network Communications (CoCoNet’20)
  • 10 invited talks:
    1. “Engineering Digital Twins in Practice,” Panelist, Workshop on Engineering Digital Twins, AMRC, University of Sheffield, UK, Dec. 2022.
    2. “Digital Twin’s State of Play: Concepts, Development and Use Cases,” Invited talk, International Workshop on Digital Twin Engineering, London, UK. (https://dt-engineering.uibk.ac.at/), 2022.
    3. “Digital Twins and its Trends Towards Smart Automation,” Invited talk, UK-INDIA Workshop on Enhancing Employability of higher Education Graduates in Industry 4.0, Jul. 2022
    4. “Information Management Framework,” Roundtable discussion, hosted by National Oceanography Centre and Met Office, London, Mar. 2022
    5. “Digital Twin and Metaverse,” Invited Talks, Viettel Group (Vietnam), Mar. and May 2022
    6. “Digital Twin Technology,” Keynote Speaker, The 2022 Van Lang International Conference on Heritage and Technology, Mar. 2022.
    7. “Innovation Conversation: Digital Twins,” Speaker, Twitter Spaces conversation hosted by Prof Lucy Rogers FREng, 19 Jan. 2022
    8. “Digital Twin for IoT based Smart Monitoring in Industry 4,” Keynote Speaker, International Workshop on Convergence Platform for IoT Based Smart Monitoring Systems, 22-23 Dec. 2021
    9. Digital Twin for the Future,” Panel Chair, IEEE Smart Cities Conference, Sep. 2021
    10. “Digital Twin for Infrastructure and Smart Manufacturing,” Keynote Speaker, ICAMEROB 2021