Using Machine Learning to Reduce Manual Incident Report Review by 60%

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Case Study

In a given year, a single nuclear power plant can generate 10,000 - 20,000 incident reports (IRs), documenting a wide range of possible situations where the safety of the plant may have been compromised. Each IR that is generated must be reviewed by an engineer to identify if a functional failure of high safety significance has occurred.
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The Problem

The review process for this determination is extremely resource-intensive and comes at a large cost to the utility. Additionally, an incorrect classification of an IR could result in additional scrutiny from regulatory authorities.

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The Solution

Our team is developing techniques to automate this review process using machine learning incident management. Data on how employees previously classified incident reports is fed into a computer, which “learns” what constitutes a functional failure by using the text and classifications as a reference.

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The Results

Thus far, this automation process has been able to reduce the amount of incident reports that need manual review by 60%, significantly decreasing the time, labor and cost required to process these reports.

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