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Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems

Author

Listed:
  • Tangbin Xia

    (Shanghai Jiao Tong University)

  • Lifeng Xi

    (Shanghai Jiao Tong University)

Abstract

In today’s competitive environment of Industry 4.0, cyber-physical systems (CPS) of various advanced manufacturing paradigms have brought new challenges to maintenance managements. Efficient prognostics and health management (PHM) policies, which can integrate both individual machine deteriorations and different manufacturing paradigms, are urgently needed. Newly proposed PHM methodologies are systematically reviewed in this paper: as the decision basis, an operating load based forecasting algorithm is proposed for machine health prognosis; at the machine level, a dynamic multi-attribute maintenance model is studied for diverse machines in CPS; at the system level, novel opportunistic maintenance policies are developed for complex flow-line production, mass customization and reconfigurable manufacturing systems, respectively. This framework of PHM methodologies has been validated in industrial implementations.

Suggested Citation

  • Tangbin Xia & Lifeng Xi, 2019. "Manufacturing paradigm-oriented PHM methodologies for cyber-physical systems," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1659-1672, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1342-2
    DOI: 10.1007/s10845-017-1342-2
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    References listed on IDEAS

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    Cited by:

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    3. Sinisterra, Wilfrido Quiñones & Cavalcante, Cristiano Alexandre Virgínio, 2020. "An integrated model of production scheduling and inspection planning for resumable jobs," International Journal of Production Economics, Elsevier, vol. 227(C).
    4. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    5. Liu, Gehui & Chen, Shaokuan & Ho, Tinkin & Ran, Xinchen & Mao, Baohua & Lan, Zhen, 2022. "Optimum opportunistic maintenance schedule over variable horizons considering multi-stage degradation and dynamic strategy," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Yiwei Wang & Jian Zhou & Lianyu Zheng & Christian Gogu, 2022. "An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 809-830, March.
    7. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.

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