Abstract
Risk analysis, as an important prerequisite of risk management, is critical to reducing occupational injuries and other related losses. However, suffering greatly from incomplete hazard identification and inaccurate probability analysis, risk analysis is considered the weakest link in risk management, which seriously affects risk evaluation and control in complex workplaces. To improve the performance of hazard identification and analysis, a data-driven risk analysis approach is established, which consists of an improved equivalent class transformation (Eclat) algorithm, a sliding window model, and a change pattern mining algorithm. Through this approach, a large number of historical hazard records are transformed into association rules composed of object keywords and deviation keywords, and information such as potential keyword combinations, conditional probabilities of potential deviations, and the change pattern of potential hazards can be extracted. The function of the approach is threefold. Firstly, the data-driven risk analysis process is designed to identify the association rules between different hazard keywords. Secondly, Eclat algorithm is optimized to calculate the frequency and probability of potential hazards, which is conducive to improving the accuracy of probability estimation. Thirdly, the change pattern is developed to analyse the hazard change trend to support the cause analysis. A practical application in a Chinese hazardous chemical manufacturer is presented. Case studies have shown that the efficiency of the improved algorithm is increased by 13.68%, and 59.66% of potential hazards can be identified in advance, and relevant information can be extracted to support risk analysis.
Highlights
Preventing and mitigating accidents, protecting employees from occupational injuries, and protecting the environment from damage is a major goal of all industries and superintendents [1]
En, the association rules related to the object can be extracted, and the possible deviation and change trend will be obtained to improve the efficiency of risk analysis
Calculate the number of keywords in frequent item sets, the number of frequent items, and the ratio between them. e ratio of keywords to frequent items is used to express the value density of frequent item sets. e higher the ratio, the more hazard information contained in frequent item sets, which is beneficial to improve the efficiency of association rule analysis. e hazard datasets in W1 are used for analysis, where the multiple minimum support thresholds are set according to 20% of the frequency of each keyword, and the single minimum support threshold is set to 2, 5, and 10. e results are shown in Table 9 and Figure 12
Summary
Preventing and mitigating accidents, protecting employees from occupational injuries, and protecting the environment from damage is a major goal of all industries and superintendents [1]. E above-mentioned articles show that converting textual data into several keywords and applying association rule algorithms for further analysis are excellent ideas for obtaining risk information. E aim of this article is to apply data mining algorithms to improve the ability of superintendents to identify and analyse hazards in complex workplaces. Hazard analysis is to calculate the probability of potential nodes and predict the change trend of these nodes Motivated by these ideas, a data-driven risk analysis approach is designed, which consists of an improved equivalent class transformation (Eclat) algorithm, sliding window model, and change pattern mining algorithm, to discover the nodes, connections, and their changes. The data-driven risk analysis process is designed to identify the association rules between different hazard keywords and solve the problem of incomplete hazard identification caused by insufficient experience.
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