计算机科学
领域(数学分析)
管道(软件)
事故(哲学)
关键词提取
集合(抽象数据类型)
事故报告
监督学习
机器学习
航空
人工智能
情报检索
数据挖掘
过程(计算)
工程类
计算机安全
人工神经网络
数学分析
操作系统
哲学
航空航天工程
认识论
程序设计语言
数学
作者
Abdhul Ahadh,Govind Vallabhasseri Binish,Rajagopalan Srinivasan
标识
DOI:10.1016/j.psep.2021.09.022
摘要
Learning from past incidents is critical to achieving and maintaining high process safety performance. Accident and incident records provide one way for learning; however, these are usually in the form of unstructured texts, which makes analysis difficult. Recently, text mining methods based on supervised learning have been proposed for analyzing accident reports; however, they require an impractically large number of labeled records as training examples. This paper proposes an automated, semi-supervised, domain-independent approach for analyzing accident reports. Given a set of user-defined classification topics and domain literature such as handbooks, glossaries, and Wikipedia articles, the method can identify domain-specific keywords and group them into topics with minimal expert involvement. These keywords and topics can then be used for various data mining purposes, including classification. The proposed approach is demonstrated using two different case studies across domains: (1) in aviation to identify the stage of flight when an accident occurs, and (2) in the process industry domain to identify the cause of pipeline accidents. The average classification accuracy of the proposed method was 80% which is comparable to that of supervised learning methods. The key benefits of this approach are that it can generate domain-specific predictive models with limited manual intervention.
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