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An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops

Author

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  • Chaoyang Zhang
  • Zhengxu Wang
  • Kai Ding
  • Felix T.S. Chan
  • Weixi Ji

Abstract

With the development of sensing and communications technology, some new features have emerged in manufacturing processes, such as highly correlated, deeply integrated, dynamically integrated, and a huge volume of data. There is a strong need to deeply excavate information from manufacturing Big Data, especially the energy consumption data, for energy-efficient manufacturing operations management and analysis. However, relevant data reduction and association analysis to support energy-efficient manufacturing are still ineffective and error-prone, especially for discrete manufacturing workshops. In this paper, an energy-aware Cyber Physical System (E-CPS) is proposed for energy Big Data analysis and recessive production anomalies detection. Firstly, E-CPS is introduced to acquire manufacturing Big Data. Then, a Big Data analysis method, including data reduction and data association analysis, is proposed to analyse the manufacturing data in the E-CPS. Considering the complexity and dynamics of manufacturing processes, an energy Big Data-driven recessive production anomalies analysis method is proposed based on deep belief networks. The proposed method in this paper realises the integrated utilisation of production Big Data and energy Big Data in the E-CPS. Further, the efficiency evaluation and recessive anomalies detection methods can be used in existing production information systems.

Suggested Citation

  • Chaoyang Zhang & Zhengxu Wang & Kai Ding & Felix T.S. Chan & Weixi Ji, 2020. "An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7059-7077, December.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:23:p:7059-7077
    DOI: 10.1080/00207543.2020.1748904
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    Citations

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    Cited by:

    1. Anwer Mustafa Hilal & Aisha Hassan Abdalla Hashim & Marwa Obayya & Abdulbaset Gaddah & Abdullah Mohamed & Ishfaq Yaseen & Mohammed Rizwanullah & Abu Sarwar Zamani, 2022. "Metaheuristics Based Energy Efficient Task Scheduling Scheme for Cyber-Physical Systems Environment," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    2. Xuan Su & Wenquan Dong & Jingyu Lu & Chen Chen & Weixi Ji, 2022. "Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    3. Ayaskanta Mishra & Amitkumar V. Jha & Bhargav Appasani & Arun Kumar Ray & Deepak Kumar Gupta & Abu Nasar Ghazali, 2023. "Emerging technologies and design aspects of next generation cyber physical system with a smart city application perspective," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(3), pages 699-721, July.
    4. Wang, Junya & Zhao, Qinfang & Ning, Ping & Wen, Shikun, 2024. "Greenhouse gas contribution and emission reduction potential prediction of China's aluminum industry," Energy, Elsevier, vol. 290(C).
    5. Dotun Adebanjo & Pei-Lee Teh & Pervaiz K Ahmed & Erhan Atay & Peter Ractham, 2020. "Competitive Priorities, Employee Management and Development and Sustainable Manufacturing Performance in Asian Organizations," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    6. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    7. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    8. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    9. Mehmet Ali Soytaş & Damla Durak Uşar & Meltem Denizel, 2022. "Estimation of the static corporate sustainability interactions," International Journal of Production Research, Taylor & Francis Journals, vol. 60(4), pages 1245-1264, February.

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