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An Intelligent Health diagnosis and Maintenance Decision-making approach in Smart Manufacturing

Author

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  • GAO, Guibing
  • ZHOU, Dengming
  • TANG, Hao
  • HU, Xin

Abstract

Unexpected risks and faults have made health diagnosis and maintenance decision-making of smart manufacturing difficult. Based on CPPS (cyber physical production systems) technology and the nonlinear kernel mapping algorithm,an intelligent health diagnosis and maintenance decision-making method is proposed for the equipment in smart manufacturing. The vulnerability of equipment is employed to conduct the health diagnosis strategies, the proposed method aids maintenance personnel in accurately diagnosing the equipment health and promptly implementing maintenance plans. This method enables maintenance personnel to understand the interactions within and across the equipment and identify the significant influencing factors without having to know the health degradation and potential disturbances of equipment. Moreover, the proposed intelligent health maintenance methodology can enhance equipment health by highlighting how anomalous factors are identified based on the vulnerability of the equipment and the CPPS technology. This paper discusses the main processes of the proposed methodology and demonstrates its application to a robot servo system.

Suggested Citation

  • GAO, Guibing & ZHOU, Dengming & TANG, Hao & HU, Xin, 2021. "An Intelligent Health diagnosis and Maintenance Decision-making approach in Smart Manufacturing," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004762
    DOI: 10.1016/j.ress.2021.107965
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    References listed on IDEAS

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    1. Shiyong Yin & Jinsong Bao & Jie Zhang & Jie Li & Junliang Wang & Xiaodi Huang, 2020. "Real-time task processing for spinning cyber-physical production systems based on edge computing," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2069-2087, December.
    2. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    3. Vega, Manuel A. & Hu, Zhen & Fillmore, Travis B. & Smith, Matthew D. & Todd, Michael D., 2021. "A Novel Framework for Integration of Abstracted Inspection Data and Structural Health Monitoring for Damage Prognosis of Miter Gates," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    4. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    5. Zheng, Rui & Chen, Bingkun & Gu, Liudong, 2020. "Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    7. Eleftheroglou, Nick & Zarouchas, Dimitrios & Loutas, Theodoros & Alderliesten, Rene & Benedictus, Rinze, 2018. "Structural health monitoring data fusion for in-situ life prognosis of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 40-54.
    8. Meissner, Robert & Rahn, Antonia & Wicke, Kai, 2021. "Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    9. Iamsumang, Chonlagarn & Mosleh, Ali & Modarres, Mohammad, 2018. "Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 118-129.
    10. Han, Xiao & Wang, Zili & Xie, Min & He, Yihai & Li, Yao & Wang, Wenzhuo, 2021. "Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    11. Gao, Guibing & Wang, Junshen & Yue, Wenhui & Ou, Wenchu, 2020. "Structural-vulnerability assessment of reconfigurable manufacturing system based on universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    12. Arismendi, Renny & Barros, Anne & Grall, Antoine, 2021. "Piecewise deterministic Markov process for condition-based maintenance models — Application to critical infrastructures with discrete-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
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    Cited by:

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    2. Yang, Xiuzhen & He, Yihai & Liao, Ruoyu & Cai, Yuqi & Ai, Jun, 2022. "Integrated mission reliability modeling based on extended quality state task network for intelligent multistate manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    3. Wang, Wenzhuo & He, Yihai & Liao, Ruoyu & Cai, Yuqi & Zheng, Xin & Zhao, Yu, 2022. "Mission reliability driven functional healthy state modeling approach considering production rhythm and workpiece quality for manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Yang, Xiuzhen & He, Yihai & Liao, Ruoyu & Cai, Yuqi & Dai, Wei, 2024. "Mission reliability-centered opportunistic maintenance approach for multistate manufacturing systems," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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