IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v234y2020i2p407-421.html
   My bibliography  Save this article

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Author

Listed:
  • Yixiao Zhao
  • Yihai He
  • Fengdi Liu
  • Xiao Han
  • Anqi Zhang
  • Di Zhou
  • Yao Li

Abstract

Predictive health analysis and diagnostics are essential functional elements of “Smart Manufacturing,†but existing studies are mostly limited to discrete manufacturing equipment without a holistic analysis on the entire system health state. In consideration of the enhancement of risk control in production quality management advocated in ISO 9001:2015, modeling and monitoring the risk during operation are the core aspects and bottlenecks for achieving prognostics. Therefore, a novel holistic operational risk modeling approach for discrete manufacturing systems is proposed by fusing the operational data to quantify the risk-oriented operational performance systematically. First, the connotation of operational risk is expounded and associated with system operational mechanism and mission reliability connotation. Second, a modeling foundation is proposed on the basis of quantifying machine, task execution and work-in-process quality from mission reliability framework in accordance with the operational data characteristics and properties. Third, operational risk evaluation is conducted using the above indicators, where reasonable aggregation of three indicators is performed by fuzzy evidence theory and the risk levels are determined. Finally, a case study is conducted to illustrate the effectiveness and advantages of the proposed approach.

Suggested Citation

  • Yixiao Zhao & Yihai He & Fengdi Liu & Xiao Han & Anqi Zhang & Di Zhou & Yao Li, 2020. "Operational risk modeling based on operational data fusion for multi-state manufacturing systems," Journal of Risk and Reliability, , vol. 234(2), pages 407-421, April.
    • Handle: RePEc:sae:risrel:v:234:y:2020:i:2:p:407-421
    DOI: 10.1177/1748006X19876519
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X19876519
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X19876519?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Li, Jianlan & Zhang, Xuran & Zhou, Xing & Lu, Luyi, 2019. "Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model," Renewable Energy, Elsevier, vol. 132(C), pages 1076-1087.
    2. Bouslah, Bassem & Gharbi, Ali & Pellerin, Robert, 2018. "Joint production, quality and maintenance control of a two-machine line subject to operation-dependent and quality-dependent failures," International Journal of Production Economics, Elsevier, vol. 195(C), pages 210-226.
    3. Cai, Baoping & Liu, Yonghong & Fan, Qian & Zhang, Yunwei & Liu, Zengkai & Yu, Shilin & Ji, Renjie, 2014. "Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network," Applied Energy, Elsevier, vol. 114(C), pages 1-9.
    4. Yihai He & Changchao Gu & Zhaoxiang Chen & Xiao Han, 2017. "Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5841-5862, October.
    5. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    6. Lei Xiao & Xiaohui Chen & Xinghui Zhang & Min Liu, 2017. "A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1893-1914, December.
    7. Yihai He & Jiaming Cui & Fengdi Liu & Panting Duan & Donglin Li, 2019. "Risk-oriented assembly quality analysing approach considering product reliability degradation," International Journal of Production Research, Taylor & Francis Journals, vol. 57(1), pages 271-284, January.
    8. Cai, Baoping & Liu, Yonghong & Liu, Zengkai & Tian, Xiaojie & Dong, Xin & Yu, Shilin, 2012. "Using Bayesian networks in reliability evaluation for subsea blowout preventer control system," Reliability Engineering and System Safety, Elsevier, vol. 108(C), pages 32-41.
    9. Zhaoxiang Chen & Yihai He & Yixiao Zhao & Xiao Han & Zhen He & Yu Xu & Anqi Zhang, 2019. "Mission reliability evaluation based on operational quality data for multistate manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 57(6), pages 1840-1856, March.
    10. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Lin & Lu, Zhiqiang & Ren, Yifei, 2020. "Joint production control and maintenance policy for a serial system with quality deterioration and stochastic demand," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    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. Chen, Liwei & Gao, Yansan & Dui, Hongyan & Xing, Liudong, 2021. "Importance measure-based maintenance optimization strategy for pod slewing system," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    4. Wu, Shengnan & Zhang, Laibin & Barros, Anne & Zheng, Wenpei & Liu, Yiliu, 2018. "Performance analysis for subsea blind shear ram preventers subject to testing strategies," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 281-298.
    5. Ye, Zhenggeng & Cai, Zhiqiang & Yang, Hui & Si, Shubin & Zhou, Fuli, 2023. "Joint optimization of maintenance and quality inspection for manufacturing networks based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    6. 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).
    7. Yan-Feng Li & Jinhua Mi & Yu Liu & Yuan-Jian Yang & Hong-Zhong Huang, 2015. "Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers," Journal of Risk and Reliability, , vol. 229(6), pages 530-541, December.
    8. repec:arp:tjssrr:2019:p:69-75 is not listed on IDEAS
    9. Mussard, Stéphane & Pi Alperin, María Noel, 2021. "Accounting for risk factors on health outcomes: The case of Luxembourg," European Journal of Operational Research, Elsevier, vol. 291(3), pages 1180-1197.
    10. Tao, Haohan & Jia, Peng & Wang, Xiangyu & Wang, Liquan, 2024. "Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    11. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    12. Havinga, Maik J.A. & de Jonge, Bram, 2020. "Condition-based maintenance in the cyclic patrolling repairman problem," International Journal of Production Economics, Elsevier, vol. 222(C).
    13. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    14. Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    15. Mangirdas Morkunas & Gintaras Cernius & Gintare Giriuniene, 2019. "Assessing Business Risks of Natural Gas Trading Companies: Evidence from GET Baltic," Energies, MDPI, vol. 12(14), pages 1-14, July.
    16. Scholz, Roland W. & Czichos, Reiner & Parycek, Peter & Lampoltshammer, Thomas J., 2020. "Organizational vulnerability of digital threats: A first validation of an assessment method," European Journal of Operational Research, Elsevier, vol. 282(2), pages 627-643.
    17. Cheng, Guoqing & Li, Ling, 2020. "Joint optimization of production, quality control and maintenance for serial-parallel multistage production systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    18. Garshasbi, Mohammad Sadeq, 2016. "Fault localization based on combines active and passive measurements in computer networks by ant colony optimization," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 205-212.
    19. Dr Jason Mwanza & Nothando Tshuma, 2023. "Mitigating Business Risk in Manufacturing SMEs: A nexus between informal and formal business risk management: A case of Bulawayo, Zimbabwe," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(1), pages 1107-1138, January.
    20. Maximilian Zarte & Agnes Pechmann & Isabel L. Nunes, 2022. "Problems, Needs, and Challenges of a Sustainability-Based Production Planning," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    21. Li, Weijun & Sun, Qiqi & Zhang, Jiwang & Zhang, Laibin, 2024. "Quantitative risk assessment of industrial hot work using Adaptive Bow Tie and Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:234:y:2020:i:2:p:407-421. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.