IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i9p1437-d801188.html
   My bibliography  Save this article

An Intelligent Fault Analysis and Diagnosis System for Electromagnet Manufacturing Process Based on Fuzzy Fault Tree and Evidence Theory

Author

Listed:
  • Jihong Pang

    (College of Business, Shaoxing University, Shaoxing 312000, China)

  • Jinkun Dai

    (College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China)

  • Yong Li

    (College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China)

Abstract

Because an electromagnet has a complex structure and manufacturing process, it is difficult to analyze the overall failure of the electromagnet. In order to solve this problem, a fault intelligent analysis and diagnosis system based on fuzzy fault tree and evidence theory is proposed in this paper. First, the failure structure and fuzzy fault tree are generated according to the experience. Second, the probability of failure caused by basic events is obtained based on the data statistics of the insufficient holding force of the electromagnet in the past. Then, the probability of the basic events is given by using the synthesis rules of evidence theory. Next, the belief interval of the basic event is set as the fuzzy number, and the intelligent analysis is completed by using the calculated fuzzy importance. Finally, the validity and feasibility of the proposed method is proved by using the failure of insufficient retention force in the electromagnet manufacturing process as an example.

Suggested Citation

  • Jihong Pang & Jinkun Dai & Yong Li, 2022. "An Intelligent Fault Analysis and Diagnosis System for Electromagnet Manufacturing Process Based on Fuzzy Fault Tree and Evidence Theory," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1437-:d:801188
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/9/1437/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/9/1437/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamzeh Soltanali & Mehdi Khojastehpour & José Torres Farinha & José Edmundo de Almeida e Pais, 2021. "An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing," Energies, MDPI, vol. 14(22), pages 1-21, November.
    2. Haikun Shang & Junyan Xu & Zitao Zheng & Bing Qi & Liwei Zhang, 2019. "A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory," Energies, MDPI, vol. 12(20), pages 1-22, October.
    3. Dui, Hongyan & Tian, Tianzi & Zhao, Jiangbin & Wu, Shaomin, 2022. "Comparing with the joint importance under consideration of consecutive-k-out-of-n system structure changes," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Long-long Song & Tai-yong Wang & Xiao-wen Song & Lei Xu & De-gang Song, 2015. "Research and Application of FTA and Petri Nets in Fault Diagnosis in the Pantograph-Type Current Collector on CRH EMU Trains," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, October.
    5. Osama Moaaz & Jan Awrejcewicz & Omar Bazighifan, 2020. "A New Approach in the Study of Oscillation Criteria of Even-Order Neutral Differential Equations," Mathematics, MDPI, vol. 8(2), pages 1-8, February.
    6. Dui, Hongyan & Zheng, Xiaoqian & Wu, Shaomin, 2021. "Resilience analysis of maritime transportation systems based on importance measures," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    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. Hongyan Dui & Jiaying Song & Yun-an Zhang, 2023. "Reliability and Service Life Analysis of Airbag Systems," Mathematics, MDPI, vol. 11(2), pages 1-13, January.

    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. Zhao, Xian & He, Zongda & Wu, Yaguang & Qiu, Qingan, 2022. "Joint optimization of condition-based performance control and maintenance policies for mission-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Hongyan Dui & Zhe Xu & Liwei Chen & Liudong Xing & Bin Liu, 2022. "Data-Driven Maintenance Priority and Resilience Evaluation of Performance Loss in a Main Coolant System," Mathematics, MDPI, vol. 10(4), pages 1-18, February.
    3. Liu, Mingli & Wang, Dan & Zhao, Jiangbin & Si, Shubin, 2022. "Importance measure construction and solving algorithm oriented to the cost-constrained reliability optimization model," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Dui, Hongyan & Zhang, Chi & Tian, Tianzi & Wu, Shaomin, 2022. "Different costs-informed component preventive maintenance with system lifetime changes," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Chen, Liwei & Cheng, Chunchun & Dui, Hongyan & Xing, Liudong, 2022. "Maintenance cost-based importance analysis under different maintenance strategies," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Hongyan Dui & Jiaying Song & Yun-an Zhang, 2023. "Reliability and Service Life Analysis of Airbag Systems," Mathematics, MDPI, vol. 11(2), pages 1-13, January.
    7. Yaguang Wu & Qingan Qiu, 2022. "Optimal Triggering Policy of Protective Devices Considering Self-Exciting Mechanism of Shocks," Mathematics, MDPI, vol. 10(15), pages 1-18, August.
    8. 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).
    9. Liao, Ruoyu & He, Yihai & Zhang, Jishan & Zheng, Xin & Zhang, Anqi & Zhang, Weifang, 2023. "Reliability proactive control approach based on product key reliability characteristics in manufacturing process," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    10. Dui, Hongyan & Wei, Xuan & Xing, Liudong & Chen, Liwei, 2023. "Performance-based maintenance analysis and resource allocation in irrigation networks," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    11. Wu, Congshan & Zhao, Xian & Wang, Siqi & Song, Yanbo, 2022. "Reliability analysis of consecutive-k-out-of-r-from-n subsystems: F balanced systems with load sharing," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    12. Wang, Xiaoyue & Ning, Ru & Zhao, Xian & Zhou, Jian, 2022. "Reliability analyses of k-out-of-n: F capability-balanced systems in a multi-source shock environment," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    13. Feng, Qiang & Liu, Meng & Dui, Hongyan & Ren, Yi & Sun, Bo & Yang, Dezhen & Wang, Zili, 2022. "Importance measure-based phased mission reliability and UAV number optimization for swarm," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    14. Dui, Hongyan & Tian, Tianzi & Wu, Shaomin & Xie, Min, 2023. "A cost-informed component maintenance index and its applications," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    15. Wen, Tao & Gao, Qiuya & Chen, Yu-wang & Cheong, Kang Hao, 2022. "Exploring the vulnerability of transportation networks by entropy: A case study of Asia–Europe maritime transportation network," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    16. Hao, Yucheng & Jia, Limin & Zio, Enrico & Wang, Yanhui & Small, Michael & Li, Man, 2023. "Improving resilience of high-speed train by optimizing repair strategies," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.
    18. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    19. Guangying Jin & Wei Feng & Qingpu Meng, 2022. "Prediction of Waterway Cargo Transportation Volume to Support Maritime Transportation Systems Based on GA-BP Neural Network Optimization," Sustainability, MDPI, vol. 14(21), pages 1-24, October.
    20. Wei Zhang & Xiaohui Yang & Yeheng Deng & Anyi Li, 2020. "An Inspired Machine-Learning Algorithm with a Hybrid Whale Optimization for Power Transformer PHM," Energies, MDPI, vol. 13(12), pages 1-17, June.

    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:gam:jmathe:v:10:y:2022:i:9:p:1437-:d:801188. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.