An intelligent decision method of quality risk in electromagnet assembly process based on digital twin and q-rung dual hesitant fuzzy number

被引:0
|
作者
Pang, Jihong [1 ]
Luo, Qiang [2 ]
Li, Yong [2 ]
Dai, JinKun [3 ]
机构
[1] Shaoxing Univ, Coll Business, Shaoxing, Peoples R China
[2] Wenzhou Univ, Coll Mech & Elect Engn, Wenzhou, Peoples R China
[3] Zhejiang Coll Secur Technol, Coll New Energy Equipment, 2555 Ouhai Ave, Wenzhou 325035, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Digital twin; FMEA; q-RDHFN; bidirectional projection method; best and worst method; CRITIC; RELIABILITY-BASED DESIGN; TECHNOLOGY; WEIGHT; SYSTEM;
D O I
10.1177/16878132241278459
中图分类号
O414.1 [热力学];
学科分类号
摘要
In order to solve the problem of online risk analysis of assembly process, an intelligent decision-making method of electromagnet assembly risk based on Digital Twin (DT) was proposed. Firstly, a DT model of electromagnet assembly process is constructed, and the potential failure modes are collected in real time at the physical layer. Secondly, in the virtual layer, real-time potential failure modes are obtained through information synchronization and transmitted to the application layer. Then, in the application layer, the optimized Failure Mode and Effects Analysis (FMEA) method is used to analyze the risk of failure mode, and the risk value of failure mode is fed back to the virtual layer. In the improved FMEA method, the q-Rung Dual Hesitant Fuzzy Number (q-RDHFN) is used as the evaluation language. The weight of experts is calculated by Bidirectional Projection Method (BPM), and the comprehensive weight of risk factors is calculated by Best and Worst Method (BWM) method, entropy weight method and Criteria Importance Through Intercriteria Correlation (CRITIC) method. Finally, the physical layer is adjusted to reduce the probability and risk of failure mode occurrence. An example of risk intelligent decision making is given to verify the objectivity and practicability of the new method.
引用
收藏
页数:20
相关论文
共 50 条
  • [1] A Novel q-Rung Dual Hesitant Fuzzy Multi-Attribute Decision-Making Method Based on Entropy Weights
    Kou, Yaqing
    Feng, Xue
    Wang, Jun
    ENTROPY, 2021, 23 (10)
  • [2] Dual Hesitant q-Rung Orthopair Fuzzy Muirhead Mean Operators in Multiple Attribute Decision Making
    Wang, Jie
    Wei, Guiwu
    Wei, Cun
    Wei, Yu
    IEEE ACCESS, 2019, 7 : 67139 - 67166
  • [3] New q-Rung Orthopair Hesitant Fuzzy Decision Making Based on Linear Programming and TOPSIS
    Yang, Wei
    Pang, Yongfeng
    IEEE ACCESS, 2020, 8 : 221299 - 221311
  • [4] A Novel Decision-Making Method for Selecting Superintendent Based on a Q-Rung Dual Hesitant Fuzzy Power Partitioned Bonferroni Mean Operator
    Chen, Tiedong
    Ye, Long
    SYMMETRY-BASEL, 2022, 14 (03):
  • [5] Hybrid decision making method based on q-rung orthopair fuzzy improved weighted geometric operator of q-rung orthopair fuzzy values
    Kaur, Gagandeep
    Bhardwaj, Reeta
    Arora, Rishu
    Kumar, Kamal
    OPSEARCH, 2023, 60 (03) : 1312 - 1330
  • [6] Hybrid decision making method based on q-rung orthopair fuzzy improved weighted geometric operator of q-rung orthopair fuzzy values
    Gagandeep Kaur
    Reeta Bhardwaj
    Rishu Arora
    Kamal Kumar
    OPSEARCH, 2023, 60 : 1312 - 1330
  • [7] Weighted dual hesitant q-rung orthopair fuzzy sets and their application in multicriteria group decision making based on Hamacher operations
    Sarkar, Arun
    Deb, Nayana
    Biswas, Animesh
    COMPUTATIONAL & APPLIED MATHEMATICS, 2022, 42 (01):
  • [8] Q-Rung Probabilistic Dual Hesitant Fuzzy Sets and Their Application in Multi-Attribute Decision-Making
    Li, Li
    Lei, Hegong
    Wang, Jun
    MATHEMATICS, 2020, 8 (09)
  • [9] Weighted dual hesitant q-rung orthopair fuzzy sets and their application in multicriteria group decision making based on Hamacher operations
    Sarkar, Arun
    Deb, Nayana
    Biswas, Animesh
    COMPUTATIONAL & APPLIED MATHEMATICS, 2023, 42 (01):
  • [10] A general framework for multi-granulation rough decision-making method under q-rung dual hesitant fuzzy environment
    Yabin Shao
    Xiaoding Qi
    Zengtai Gong
    Artificial Intelligence Review, 2020, 53 : 4903 - 4933