2016年至2022年期间Rostechnadzor活动领域经常发生的违反强制性要求的行为分析

Q3 Engineering
A. Tyurin
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引用次数: 0

摘要

报告了2016-2022年期间联邦环境、工业和核监督局活动领域频繁违反强制性要求的研究结果。介绍了在国内外实践中用于分析该领域数据的主要方法。使用根据源数据中提出的特定标准将收集到的材料分为组和亚组,并按要求的顺序排列数据的方法,调查已发现的违规行为的原因和类型。确定了不同类型的国家监管(在核能使用、能源监管、建筑监管、工业安全监管领域)发现的违规行为的扩大群体。该研究的目的是系统化2016-2022年期间的公开数据,以便随后根据后果的严重程度,确定各部门记录的违规案件数量的分布,即扩大的违规群体检测到的违规行为的百分比。在检查数据是否符合帕累托规则时,结果表明,一方面,13%的检测到的违反强制性要求的类型导致检测到的违规总数的80%,另一方面,13.3%的主要违规原因也导致检测到违规总数的80%。所提供的公开数据的参数已经确定,完成这些参数可以在分析已确定的违规行为以及关键类型的违规行为时对所获得的值进行细化,对于这些违规行为,没有完善的方法来评估后果的风险和负面后果的严重性。
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 Frequently Occurring Violations of the Mandatory Requirements in the Field of Activity of Rostechnadzor for the Period 2016–2022
The results of studies of frequent violations of mandatory requirements in the field of activity of the Federal Environmental, Industrial and Nuclear Supervision Service for the period 2016–2022 are presented. The main approaches used to analyze data in this area, which are found in the domestic and international practice, are described. Using the methods of structuring the collected materials considering the division into groups and subgroups in accordance with a certain criterion presented in the source data, and arranging the data in the required order, the causes and types of identified violations are investigated. The enlarged groups of violations detected by different types of state supervision (in the field of nuclear energy use, energy supervision, construction supervision, industrial safety supervision) are identified. The purpose of the study is to systematize open data for the period of 2016–2022 for the subsequent identification of the distribution of the number of recorded cases of violations in various sections — according to the severity of the consequences, as a percentage of detected violations by enlarged groups of violations. When checking the data for compliance with the Pareto rule, it is shown that, on the one hand, 13 % of the detected types of violations of mandatory requirements lead to 80 % of the total number of violations detected, on the other hand, 13.3 % of the main causes of violations also lead to 80 % of the total number of violations detected. The parameters of the presented open data are identified, the completion of which can lead to a refinement of the obtained values in the analysis of the identified violations, as well as the key types of violations, for which there are no well-developed methods for assessing the risk of consequences and the severity of negative consequences.
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来源期刊
Bezopasnost'' Truda v Promyshlennosti
Bezopasnost'' Truda v Promyshlennosti Environmental Science-Environmental Science (miscellaneous)
CiteScore
1.00
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0.00%
发文量
110
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