DESC0025145

Nuclear Energy has the potential to significantly change the energy production landscape in the United States, meeting the nationĺs energy demands in a sustainable and environmentally responsible manner. However, the aging plants necessitate improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. QSI proposes to leverage its expertise in Integrated Diagnostics and Prognostics and Data-driven techniques to develop a Digital Twin model-based based Remote Diagnostic, Prognostic and Decision Support solution for NPPs that reduces overall Operations and Maintenance (O&M) costs due to unplanned downtime and increases operational availability. The solution will employ model-based techniques to reason across these operating characteristics to provide a system-wide picture of the overall system health. Additionally, real-time monitoring engines will provide actionable maintenance alerts and provide advanced warnings of impending failure so that the sustainment team can schedule a maintenance before the component fails. During Phase I, QSI will focus on the following tasks: 1. Investigate digital twin modeling and training requirements with Energy Systems providers and subject matter experts (SMEs) for testing and validating the proposed solution. 2. Build a suitable cause-effect model comprised of nuclear plant components such steam generator, feed water pump, turbine generator, chill water system, etc. 3. Investigate current maintenance procedures to gather component failure-frequency and downtime-per-failure metrics of nuclear plant components to determine MTTF (Mean-Time-to-Failure) and MTTR (Mean-Time-to-Repair) values in the cause-effect model. 4. Investigate interfaces with nuclear plant sensor systems. 5. Investigate Machine Learning techniques to detect anomalies in the standard operating characteristics. Key Advantages to QSIĺs Remote Diagnostic/Prognostic Solution are: Ľ Health Monitoring and Maintenance Dashboard provides real-time status of the nuclear plant to the maintenance crew, with customized alerts. Ľ Agile Process that powers troubleshooting sessions driven by intelligent reasoners. As the plant modernizes and becomes increasingly complex, the Digital Twin is continuously improved. Ľ Sensor Placement Recommendations to maximize diagnosability and fault-isolation. Ľ Minimize reduced unplanned/unscheduled maintenance. Ľ False Alarms are minimized by system-wide cause-effect relationships leading to reliable diagnostics. Ľ Explainability for ML algorithms to understand the cause-effect relationships of faults/anomalies. Ľ Minimize Regulatory Footprint - passive monitoring, opportunistic maintenance in existing schedules Ľ Single source of truth used across multiple life-cycle development of the digital twin: Concept; Design; Development; Testing; Deployment; Maintenance and Operations. Ľ Workflow management allows technicians with various skill levels to manage work-orders. Ľ Predictive maintenance methodologies can predict premature component failures and provides advanced warnings so that they can be fixed via Scheduled and Opportunistic maintenance. Ľ Diagnose Before Dispatch ľ The solution will generate an optimized troubleshooting strategy based on online health, along with anticipated service parts and necessary resources, the right technician skillset required, materials, knowledge and tools so that the problem can be corrected during the maintenance visits. The solution will guide the maintenance crew with step-by-step Guided Troubleshooting instructions to ensure that the problem is diagnosed, fixed and service restored in shortest possible time. Commercial Applications: Ľ Nuclear Energy suppliers e.g., Public Service Enterprise Group, Tennessee Valley Authority, etc. Our solution, with potential to decrease O&M costs, could help suppliers operate their plants competitively. Ľ Suppliers of nuclear reactors such as GE-Hitachi, Westinghouse, etc. Ľ Building and facility controls (HVAC, etc.), Industrial Control systems, Naval Control Systems, etc. Ľ Contractors/suppliers involved with maintenance of oil and natural gas offshore platforms.