The insights from this study on causality extraction from nuclear licensee event reports can be leveraged to enhance predictive maintenance and risk analysis models for critical equipment and systems in nuclear power plants by:
Early Fault Detection: Use the extracted cause-effect pairs to identify potential causes of equipment failures or malfunctions before they occur, enabling proactive maintenance actions.
Root Cause Analysis: Analyze historical data on causal relationships to determine the root causes of recurrent issues and implement targeted solutions to prevent future occurrences.
Risk Mitigation: Incorporate the identified causal relationships into risk analysis models to assess the impact of equipment failures on plant operations and safety.
Predictive Maintenance: Develop predictive maintenance schedules based on the extracted causal insights to optimize equipment performance and minimize downtime.
Decision Support Systems: Integrate the causality extraction framework into decision support systems for plant operators, providing real-time insights into potential risks and maintenance requirements.
By leveraging the causality extraction framework developed in this study, nuclear power plant operators can enhance the reliability, safety, and efficiency of their operations through data-driven predictive maintenance and risk analysis strategies.