使用 HFACS- 矿业框架方法分析 2018-2022 年期间 X 号坪的事故数据

Agus Winarko, Zulkifli Djunaidi
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摘要

采矿业是一项高风险的工业活动。人为因素被认为是采矿业重大事故的最常见原因。PT X公司是一家从事铜、金、银精矿勘探、开采、加工和销售的公司,隶属于国有矿业控股企业印度尼西亚矿业公司,其事故管理系统记录了2018-2022年间的322起事故案例。本研究旨在使用人为因素分析和分类系统--采矿业(HFACS-MI)框架分析 PT X 的事故数据。方法。本研究从事故管理系统数据库中收集了 2018-2022 年 PT X 的 322 起事故案例的定性数据,并将其归类为可记录伤害。使用 HFACS-MI 框架对导致事故的因素进行编码。事故数据分析采用描述性统计。研究结果使用 HFACS-MI 框架进行分析的结果显示,每一层或每一级都是事故的诱因,即外部因素占 44%,组织影响占 68%,不安全领导占 90%,不安全行为的先决条件占 99%,不安全行为占 99.7%。结论。这些发现强调了减少采矿作业中人为失误的必要性,以降低当前的事故趋势。事实证明,HFACS-MI 框架是对采矿中的人为因素进行有力事故分析的宝贵工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANALYSIS OF ACCIDENT DATA AT PT X FOR THE 2018-2022 PERIOD USING THE HFACS-MINING INDUSTRY FRAMEWORK METHOD
The mining industry is a high-risk industrial activity. Human factors have been identified as the most common cause of major accidents in the mining industry. The Incident Management System of PT X, which is a company in exploration, mining, processing, and marketing sector of copper, gold and silver concentrate, part of the state-owned mining holding enterprise Mining Industry Indonesia, documented 322 cases of accidents within the 2018-2022 period. This research aims to analyze accident data at PT X using the Human Factor Analysis and Classification System-Mining Industry (HFACS-MI) framework. Methods. This research collected qualitative data for 322 accident cases at PT X in 2018-2022 from the incident management system database categorized as recordable injuries. Factors causing the accident were coded using the HFACS-MI framework. Accident data analysis used descriptive statistics. Results. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved permanent workers at PT X. The results of the analysis using the HFACS-MI framework showed that each layer or level contributes to accidents, namely external factors by 44%, organizational influence by 68%, unsafe leadership by 90%, preconditions for unsafe acts by 99%, and unsafe acts by 99.7%. Conclusion. These findings emphasize the need to reduce the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.
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