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- W3005283220 abstract "Many companies maintain large databases of incident reports, which are collected over many years. These reports tend to be stored in databases, with some descriptive analysis, but not in-depth examination of trends or leading indicators. Incidents can be reported more accurately, aggregated, and analyzed across companies to better understand, prevent, and mitigate risks. The aim of this research is to create a risk matrix system for collectively analyzing incident reports, commensurate across companies, for increased reliability in reporting and enhanced analytical power across an industry. Then, a supervised machine learning approach is applied in conjunction with this risk matrix to analyze incident reports and increase process safety. During this research project, 15,000 incident reports, including both process and occupational-type incidents, were analyzed from five oil sand companies across Alberta. The results were classified by incident type (determined by industry experts) and consequence type (using the risk matrix). Furthermore, potential and actual risk scores were evaluated for every incident using the risk matrix. This analysis built the foundation for a system to identify trends and leading indicators, and to design prevention and mitigation strategies across the entire industry." @default.
- W3005283220 created "2020-02-14" @default.
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- W3005283220 date "2020-03-01" @default.
- W3005283220 modified "2023-10-17" @default.
- W3005283220 title "Seeing the forest and the trees: Using machine learning to categorize and analyze incident reports for Alberta oil sands operators" @default.
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- W3005283220 doi "https://doi.org/10.1016/j.jlp.2020.104069" @default.
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