Incident Report Classification for Faster Abnormal Condition Detection
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Solution Overview
Problem
Operators in industrial facilities face overwhelming volumes of incident reports, making it difficult to identify potentially hazardous abnormal conditions in a timely manner due to insufficient time for thorough review.
Innovation Solution
A method that divides incident reports into text portions, assigns confidence values, determines report characteristics, and trains a neural network model to output confidence values, generating a user interface with visual representations to flag reports based on thresholds, balancing false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators manually review all incident reports to identify abnormal conditions, then detection accuracy is improved, but time consumption and operator workload increase significantly
Solution Approach 1:
The incident report analysis is segmented into multiple components: text portions are divided into segments, each assigned confidence values; report characteristics are extracted separately; and a neural network model processes these segmented elements independently before integrating results. This segmentation allows parallel processing and reduces the time required for comprehensive review while maintaining detection accuracy.
Solution Approach 2:
A neural network model serves as an intermediary between the raw incident reports and the final detection results. The model automatically processes and prioritizes reports based on learned patterns, acting as a mediator that filters and ranks information before human operators review only the most critical cases. This intermediary layer significantly reduces time consumption while preserving detection accuracy for abnormal conditions.
2Reliability
If operators review all incident reports thoroughly to ensure no hazardous conditions are missed, then detection completeness is improved, but productivity decreases due to overwhelming volume
Solution Approach 1:
The system performs preliminary analysis by automatically assigning confidence values to text portions and determining report characteristics before human review. This preliminary action filters and prioritizes incident reports, presenting only the most relevant cases to operators. As a result, detection completeness is maintained for critical cases while productivity increases by reducing the volume of reports requiring manual review.
Solution Approach 2:
The manual mechanical process of reviewing all incident reports is replaced with an automated neural network system that processes reports at machine speed. This substitution maintains detection completeness by systematically evaluating all reports for key characteristics while dramatically increasing productivity through automated high-volume processing.
3Productivity
If a simple filtering system is used to prioritize incident reports, then processing speed is improved, but measurement precision of abnormal conditions deteriorates
Solution Approach 1:
The system uses multiple parameters including confidence values for text portions, report characteristics, and aggregated confidence scores to prioritize incident reports. By changing from simple single-parameter filtering to multi-parameter analysis, the system maintains measurement precision for abnormal conditions while achieving high processing speed through automated neural network evaluation of all parameters.
Solution Approach 2:
The filtering system combines multiple data elements (text confidence values, report characteristics, neural network outputs) into a composite assessment of each incident report. This composite approach maintains classification accuracy by considering multiple factors simultaneously while achieving fast processing through automated integration of all parameters by the neural network model.
Data Source
AI summary
A method for monitoring an industrial facility divides an incident report into text portions. The method determines text confidence values to the text portions. The method determines a report characteristic including a non-textual data type for the incident report. The method trains a neural network model. The method inputs the text confidence values and the report characteristic into the neural network model. The method outputs a network confidence value from the neural network model in response to inputting the text confidence values and the report characteristic.