Feature mining and causative analysis of fire accidents in ship repair and building enterprises

doi:10.16265/j.cnki.issn1003-3033.2024.03.0368

Abstract

Abstract:

To investigate the features and causation factors of fire accidents in SRBE, 118 fire accidents were studied using method of mathematical statistics. Firstly, accident features were mined in terms of the temporal and spatial attributes of accidents, the three elements of fire and the severity of injuries. Then, based on the improved HFACS-SRBE accident causation model, methods such as Chi-square test, OR and Cramer's V were used to analyze the correlations and their strength between the causal factors. The results show that 14:00-16:00 is the most prone to fire accidents and casualties. The main location of fire accidents is in the ship area, accounting for 33.90%. 59.19% of the ignition source comes from the ignition operation. Ratio of no injuries, slight and minor injuries, serious injuries and deaths is 61:54:3. Through the test of correlations and their strength, it is determined that 15 groups of adjacent level causal factors are correlated, and 26 complete accident causation chains are obtained. Covers communication and coordination→inappropriate operation plans→bad psychological state→decision-making errors is the accident causation chain with the greatest strength of correlation leading to the occurrence of fire accidents in ship repair and building enterprises.

Key words: ship repair and building enterprises(SRBE), fire accident, causation analysis, human factors analysis and classification system (HFACS) model, Chi-square test, odds ratio (OR), Cramer's V

CLC Number:

LI Xian, JIAO Yu, CHEN Wentao, SHI Danda, KANG Yutao. Feature mining and causative analysis of fire accidents in ship repair and building enterprises[J]. China Safety Science Journal, 2024, 34(3): 29-38.

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References 21

[1]	Occupational Safety and Health Administration (OSHA). Shipyard employment: shipbuilding[EB/OL]. [2024-01-24]. https://www.osha.gov/etools/shipyard/shipbuilding.
[2]	陈刚, 张晓蕾, 徐帅, 等. 我国2005—2020年粉尘爆炸事故统计分析[J]. 中国安全科学学报, 2022, 32(8):76-83. doi: 10.16265/j.cnki.issn1003-3033.2022.08.0812
	CHEN Gang, ZHANG Xiaolei, XU Shuai, et al. Statistical analysis on dust explosion accidents in China from 2005 to 2020[J]. China Safety Science Journal, 2022, 32(8):76-83. doi: 10.16265/j.cnki.issn1003-3033.2022.08.0812
[3]	李敏, 林志军, 王德明, 等. 我国煤矿重特大火灾事故统计分析[J]. 中国安全科学学报, 2023, 33(1):115-121. doi: 10.16265/j.cnki.issn1003-3033.2023.01.0457
	LI Min, LIN Zhijun, WANG Deming, et al. Statistical analysis of major coal mine fire accidents in China[J]. China Safety Science Journal, 2023, 33(1):115-121. doi: 10.16265/j.cnki.issn1003-3033.2023.01.0457
[4]	丁永明, 朱靳良, 傅华. 造船企业工伤事故调查[J]. 中国工业医学杂志, 2000, 18(5):284-285.
[5]	王颖丽, 夏昭林, 庄惠民. 某造船企业1994—2005年职业伤害流行病学调查[J]. 环境与职业医学, 2008, 25(1):49-50.
	WANG Yinli, XIA Zhaolin, ZHUANG Huimin. Epidemiological study of occupational injuries in a shipyard during the period of 1994-2005[J]. Journal of Environmental and Occupational Medicine, 2008, 25(1):49-50.
[6]	YONG J B. Prevalence of occupational accidents and factors associated with deaths and disabilities in the shipbuilding industry: comparisons of novice and skilled workers[J]. WORK-A Journal of Prevention Assessment & Rehabilitation, 2021, 69(3):997-1005.
[7]	YOU W S, JEONG B Y, LEE D K. Comparison of occupational accidents and illnesses in shipbuilding industry[J]. Journal of the Ergonomics Society of Korea, 2019, 38(5):385-392. doi: 10.5143/JESK.2019.38.5.385
[8]	郑霞忠, 柯善钢, 陈星, 等. 基于复杂网络的内河船舶碰撞事故致因分析[J]. 中国安全生产科学技术, 2023, 19(2):211-217.
	ZHENG Xiazhong, KE Shangang, CHEN Xing, et al. Cause analysis of inland river ship collision accident based on complex network[J]. Journal of Safety Science and Technology, 2023, 19(2):211-217.
[9]	刘子涛, 杜柏松, 贾帅林. 基于贝叶斯网络的船舶大风浪中倾覆事故致因分析[J]. 上海海事大学学报, 2022, 43(3):56-61.
	LIU Zitao, DU Baisong, JIA Shuailin. Analysis on causes of ship capsizing accidents in heavy winds and waves based on Bayesian network[J]. Journal of Shanghai Maritime University, 2022, 43(3):56-61.
[10]	肖仲明, 王新建, 章文俊. 基于贝叶斯网络模型的船舶搁浅事故分析[J]. 安全与环境学报, 2017, 17(2):418-421.
	XIAO Zhongming, WANG Xinjian, ZHANG Wenjun. Analysis for the ship grounding accidents based on the Bayesian network model[J]. Journal of Safety and Environment, 2017, 17(2):418-421.
[11]	GB/T 6441—1986, 企业职工伤亡事故分类标准[S].
	GB/T 6441-1986, Classification for casualty accidents of enterprise staff and workers[S].
[12]	GB/T 6442—1986, 企业职工伤亡事故调查分析规则[S].
	GB/T 6442-1986, Rules for investigation and analysis of casualty accidents of enterprise staff and workers[S].
[13]	WIEGMANN D A, SHAPPELL S A. Human error analysis of commercial aviation accidents: application of the human factors analysis and classification system (HFACS)[J]. Aviation, Space, and Environmental Medicine, 2001, 72(11):1006-1016. pmid: 11718505
[14]	刘全龙, 彭雨蒙, 赵盼, 等. 基于HFACS模型的制造企业机械伤害事故致因分析[J]. 中国安全科学学报, 2023, 33(3):51-59. doi: 10.16265/j.cnki.issn1003-3033.2023.03.1128
	LIU Quanlong, PENG Yumeng, ZHAO Pan, et al. Analysis of causes of mechanical injury accidents in manufacturing enterprises based on HFACS model[J]. China Safety Science Journal, 2023, 33(3):51-59. doi: 10.16265/j.cnki.issn1003-3033.2023.03.1128
[15]	王晶, 樊运晓, 高远. 基于HFACS模型的化工事故致因分析[J]. 中国安全科学学报, 2018, 28(9):81-86. doi: 10.16265/j.cnki.issn1003-3033.2018.09.014
	WANG Jing, FAN Yunxiao, GAO Yuan. Analysis of causes of accidents in chemical industry based on HFACS model[J]. China Safety Science Journal, 2018, 28(9):81-86. doi: 10.16265/j.cnki.issn1003-3033.2018.09.014
[16]	郑世博, 樊运晓, 李振明. 基于HFACS的货车交通事故致因研究[J]. 安全与环境工程, 2020, 27(6):133-139.
	ZHENG Shibo, FAN Yunxiao, LI Zhenming. Study on the causes of freight car traffic accidents based on HFACS[J]. Safetyand Environmental Engineering, 2020, 27(6):133-139.
[17]	SHAO Bo, HU Zhigen, LIU Quan, et al. Fatal accident patterns of building construction activities in China[J]. Safety Science, 2018, 111:253-263. doi: 10.1016/j.ssci.2018.07.019
[18]	BARLAS B, IZCI F B. Individual and workplace factors related to fatal occupational accidents among shipyard workers in Turkey[J]. Safety Science, 2018, 101:173-179. doi: 10.1016/j.ssci.2017.09.012
[19]	公安部刑事侦查局. 人体损伤程度鉴定标准释义[M]. 北京: 中国人民公安大学出版社, 2013:2-4.
[20]	GB/T 4968—2008, 火灾分类[S].
	GB/T 4968-2008, Classification of fires[S].
[21]	YANG Li, WANG Xue, ZHU Junqi, et al. Risk factors identification of unsafe acts in deep coal mine workers based on grounded theory and HFACS[J]. Frontiers in Public Health, 2022, 10: 1-15.

空间特征	内场	厂区外	码头或坞	船舶	车间	总计
船体车间	12(9)	0(0)	15(4)	20(14)	0(0)	47(27)
机电车间	6(2)	0(0)	10(4)	7(4)	0(0)	23(10)
涂装车间	2(1)	0(0)	3(3)	2(1)	3(2)	10(7)
搭载车间	2(2)	0(0)	1(1)	2(2)	0(0)	5(5)
船台车间	1(0)	0(0)	1(1)	3(1)	0(0)	5(2)
建造部	4(4)	0(0)	0(0)	0(0)	0(0)	4(4)
分段车间	3(3)	0(0)	0(0)	1(0)	0(0)	4(3)
内业车间	1(1)	0(0)	2(0)	0(0)	1(1)	4(2)
舾装车间	0(0)	0(0)	2(2)	2(1)	0(0)	4(3)
机修车间	1(1)	0(0)	0(0)	1(2)	0(0)	2(3)
行政部	1(0)	1(0)	0(0)	0(0)	0(0)	2(0)
资产部	2(1)	0(0)	0(0)	0(0)	0(0)	2(1)
总务部	1(1)	0(0)	0(0)	0(0)	0(0)	1(1)
事业部	0(0)	0(0)	1(0)	0(0)	0(0)	1(0)
物资部	1(0)	0(0)	0(0)	0(0)	0(0)	1(0)
质量部	0(0)	0(0)	0(0)	0(0)	1(1)	1(1)
轮机车间	0(0)	0(0)	0(0)	1(1)	0(0)	1(1)
上建车间	0(0)	0(0)	0(0)	1(0)	0(0)	1(0)
总计	37(25)	1(0)	35(15)	40(27)	5(4)	118(71)

点火源分类		分类说明	事故起数	事故数量子分类占比/%	事故数量整体占比/ %
直接火源	明火	电焊火星	22	25.88	18.64
		气/切割火星	27	31.76	22.88
		高温铁屑/水/块	10	11.76	8.47
		割炬/烘枪/ 烤把火焰	23	27.06	19.49
		未熄灭的烟头	3	3.53	2.54
	电火花	电气设备产生的电弧和静电火花	8	100	6.78
	雷击	瞬间的高压放电	0	100	0
间接火源	加热自燃起火	热表面	3	13.64	2.54
		热辐射	7	31.82	5.93
		线路发热起火	10	45.45	8.47
		摩擦撞击压缩起火	2	9.09	1.69
		聚焦起火	0	0	0
	本身自燃起火 (无明火和外来热源)	物质本身自燃起火	0	0	0
	本身自燃起火 (无明火和外来热源)	化学反应热腐蚀/ 自燃起火	3	100	2.54

可燃物分类	分类说明	事故起数	事故起数子分类占比/%	事故起数整体占比/%
设备财产	电器设备	14	16.67	11.86
	油布/三防布/回丝布等布料	14	5.95	11.86
	油箱/塑料桶/ 管线/管带	25	3.57	21.19
	化学品	14	8.33	11.86
	麻绳/安全网	5	2.38	4.24
	手套/防护服/劳保鞋等防护品	3	10.71	2.54
	木质/纸类材料	7	1.19	5.93
	生活/作业废弃物	2	3.57	1.69
人体组织	面颌部	9	32.14	7.63
	脑	1	3.57	0.85
	眼睛	3	10.71	2.54
	耳朵	2	7.14	1.69
	腕及手	13	46.43	11.02
设备财产+ 人体组织	衣物+上体	21	52.5	17.8
设备财产+ 人体组织	衣物+下体	19	47.5	16.1

标准分类	修理改装	其他	船舶与海工建造	总计
无伤害事故	19	12	30	61
微伤事故	14	9	20	43
轻伤事故	6	2	3	11
重伤事故	0	0	1	1
死亡事故	1	0	1	2
总计	40	23	55	118

致因因素	卡方检验		Cramer's V	OR	95%置信区间
致因因素	χ²	p	Cramer's V	OR	下限	上限
组织影响层级和不安全监督层级
L_4-1和L_3-2	23.178	0.000	0.443	8.9	3.4	23.7
L_4-2和L_3-2	23.356	0.000	0.445	10.3	3.6	29.4
L_4-3和L_3-2	12.454	0.000	0.325	5.3	2.0	14.2
L_4-3和L_3-3	6.320	0.012	0.231	2.7	1.2	5.8
L_4-4和L_3-2	5.672	0.017	0.219	1.5	1.3	1.7
不安全监督层级和不安全行为的前提条件层级
L_3-2和L_21-2	10.551	0.001	0.299	6.7	1.9	23.6
L_3-2和L_22-2	9.042	0.003	0.277	4.0	1.6	10.2
L_3-2和L_21-4	10.582	0.001	0.299	4.1	1.7	9.7
L_3-3和L_21-4	5.434	0.020	0.215	3.1	1.2	8.4
不安全行为的前提条件层级与不安全行为层级
L_21-2和 L_11-1	8.016	0.005	0.261	3.4	1.4	7.9
L_21-2和 L_11-2	12.092	0.001	0.320	4.1	1.8	9.4
L_21-2和 L_11-3	6.152	0.013	0.228	3.6	1.3	10.2
L_21-2和 L_12-1	10.012	0.002	0.291	3.7	1.6	8.5
L_21-4和 L_11-2	6.982	0.008	0.243	3.4	1.3	8.7
L_23-3和 L_11-1	5.220	0.022	0.210	3.0	1.1	7.5