Scholarlink

<p>Human error probabilities (HEP) are heavily based on the expert’s knowledge and experience in real-world scenarios. When professionals employ linguistic labels to assess human failure occurrences, uncertainty and hesitation often occur. This work seeks to quantitatively analyze human errors using a new model based on hesitant fuzzy sets theory and the Cognitive reliability and error analysis method (CREAM). To enhance the traditional model, a dynamic weight adjustment mechanism is introduced to capture the time-varying importance of Common Performance Conditions (CPCs) across different phases of an accident. This model utilizes a structured expert assessment protocol (detailed in <InternalRef RefID="Sec19">Appendix A</InternalRef>) to collect data. The evaluations from three domain experts are recorded using a hesitant fuzzy matrix (HFM) to capture their hesitancy. The model then dynamically calculates CPC weights through a constrained optimization algorithm and derives a continuous HEP estimation formula. Finally, the effectiveness and practicality of the proposed DHFM-CREAM model are demonstrated by analyzing an electrical maloperation accident in a substation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Dynamic Hesitant Fuzzy CREAM Approach for Quantifying Human Error in Substation Operation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月24日

查看全文
  • Chuan Lin,
  • Zhihui Yu

摘要

Human error probabilities (HEP) are heavily based on the expert’s knowledge and experience in real-world scenarios. When professionals employ linguistic labels to assess human failure occurrences, uncertainty and hesitation often occur. This work seeks to quantitatively analyze human errors using a new model based on hesitant fuzzy sets theory and the Cognitive reliability and error analysis method (CREAM). To enhance the traditional model, a dynamic weight adjustment mechanism is introduced to capture the time-varying importance of Common Performance Conditions (CPCs) across different phases of an accident. This model utilizes a structured expert assessment protocol (detailed in Appendix A) to collect data. The evaluations from three domain experts are recorded using a hesitant fuzzy matrix (HFM) to capture their hesitancy. The model then dynamically calculates CPC weights through a constrained optimization algorithm and derives a continuous HEP estimation formula. Finally, the effectiveness and practicality of the proposed DHFM-CREAM model are demonstrated by analyzing an electrical maloperation accident in a substation.