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

Zhao Chufan, Mi Chuanmin () and Xu Jie
Additional contact information
Zhao Chufan: Nanjing University of Aeronautics and Astronautics
Mi Chuanmin: Nanjing University of Aeronautics and Astronautics
Xu Jie: Jiangsu Electric Power Co., Ltd.

A chapter in AI and Analytics for Public Health, 2022, pp 303-315 from Springer

Abstract: 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.

Keywords: Electric power personal accident; HFACS; LCA; Accident causes; Accident characteristics (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-75166-1_22

Ordering information: This item can be ordered from
http://www.springer.com/9783030751661

DOI: 10.1007/978-3-030-75166-1_22

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().