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Electric Power Personal Accident Characteristics Recognition Based on HFACS and Latent Class Analysis

In: AI and Analytics for Public Health

Author

Listed:
  • Zhao Chufan

    (Nanjing University of Aeronautics and Astronautics)

  • Mi Chuanmin

    (Nanjing University of Aeronautics and Astronautics)

  • Xu Jie

    (Jiangsu Electric Power Co., Ltd.)

Abstract

Electric power industry is an important basic industry of national economy and an important public utility. Electric power safety is directly related to the development of national economy and the safety of people’s life and property. In order to improve the capacity of producing electric energy safely and reduce the number of electric power personal accidents, this paper focuses on the human factor of electric power accident. Firstly, a new framework suitable for power electric personal accident analysis is constructed by using the human factors analysis and classification system (HFACS). Secondly, 173 cases of accidents from 2015 to 2018 are analysed. Thirdly, the latent class analysis (LCA) method is used to cluster these cases to find out the hidden category characteristics and analyse the correlation between the causes of particular category accidents. Finally, the corresponding management countermeasures and suggestions are put forward according to different situations. The results show that, in addition to the general characteristics of power personal accidents, five major accident categories are identified by LCA method, which have their own prominent characteristics and different causative processes in terms of human factors.

Suggested Citation

  • Zhao Chufan & Mi Chuanmin & Xu Jie, 2022. "Electric Power Personal Accident Characteristics Recognition Based on HFACS and Latent Class Analysis," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), AI and Analytics for Public Health, pages 303-315, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-75166-1_22
    DOI: 10.1007/978-3-030-75166-1_22
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