中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (5): 70-75.doi: 10.16265/j.cnki.issn1003-3033.2017.05.013

• 安全工程技术科学 • 上一篇    下一篇

机务维修人员疲劳风险评价模型及管理系统实现

汪磊 副研究员, 任勇   

  1. 中国民航大学 飞行技术学院,天津 300300
  • 收稿日期:2017-01-09 修回日期:2017-03-27 出版日期:2017-05-20 发布日期:2020-10-30
  • 作者简介:任 勇 (1993—),男,陕西宝鸡人,硕士研究生,研究方向为航空安全与人为因素。E-mail:yrencauc@outlook.com。汪 磊 (1982—),男,安徽霍山人,博士,副研究员,硕士生导师,国家注册安全工程师,美国佛罗里达理工学院访问学者,主要从事航空安全与人因研究。E-mail: wanglei0564@hotmail.com。
  • 基金资助:
    中国民航局科技项目(MHRD20140206);中国民航局重点科技项目(MHRD2011238);中央高校基本科研项目(3122016B007)。

Fatigue risk evaluation model and system for civil aviation maintenance personnel

WANG Lei, REN Yong   

  1. Flight Technology College, Civil Aviation University of China, Tianjin 300300, China
  • Received:2017-01-09 Revised:2017-03-27 Online:2017-05-20 Published:2020-10-30

摘要: 为量化评价民航机务维修人员疲劳风险,首先通过调研和德尔菲法构建机务维修人员疲劳影响因素评价指标体系,依据灰色聚类原理,建立民航机务维修人员疲劳风险灰色聚类评价模型;然后从维修单位采集实际数据,结合各指标权重,通过算例验证模型的有效性;最后将模型应用于机务维修人员疲劳风险管理系统的开发中,实现调查问卷的电子化和评价过程的自动化计算。结论表明:用灰色聚类模型可实现机务维修人员疲劳风险的量化评价与分级,系统软件可作为机务维修单位疲劳风险管理的实际工具进一步推广应用。

关键词: 机务维修人员, 疲劳风险, 风险评价, 灰色聚类, 疲劳风险管理系统

Abstract: For the sake of evaluating fatigue risk of civil aviation maintenance personnel in a quantitative way, firstly an evaluation index system of fatigue inducing factors was developed based on Delphi method and a fatigue risk evaluation model for aviation personnel was built based on the gray clustering principles. Then the practical data were collected from a certain maintenance company and a case study was conducted for validating the model. Finally, the model was integrated into the development of a fatigue maintenance management system where the electronic questionnaire processing and the automated calculation process were realized. The findings of this study indicate that this gray clustering model could be used to quantitatively evaluate fatigue risk of maintenance personnel and the system could be used by maintenance organizations as a practical tool for fatigue risk management.

Key words: maintenance personnel, fatigue risk, risk evaluation, gray clustering, fatigue risk management system

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