Nancy J. Lybeck

Prognostics and life beyond 60 years for nuclear power plants

Safe, secure, reliable, and sustainable energy supply is vital for advanced and industrialized life styles. To meet growing energy demand there is interest in longer-term operation for the existing nuclear power plant fleet and enhancing capabilities in new build. There is increasing use of condition-based maintenance for active components and growing interest in deploying on-line monitoring instead of periodic in-service inspection for passive systems. Opportunities exist to move beyond monitoring and diagnosis based on pattern recognition and anomaly detection to prognostics with the ability to provide an estimate of remaining useful life. The adoption of digital I&C systems provides a framework within which added functionality including on-line monitoring can be deployed, and used to maintain and even potentially enhance safety, while at the same time improving planning and reducing both operations and maintenance costs.

Development of asset fault signatures for Prognostic and Health Management in the nuclear industry

Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institutes Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: (1) Diagnostic Advisor, (2) Asset Fault Signature Database, (3) Remaining Useful Life Advisor, and (4) Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The Asset Fault Signature Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.

Diagnostic and Prognostic Models for Generator Step-Up Transformers

................................................................................................................................................. v SUMMARY ................................................................................................................................................ vii ACKNOWLEDGMENTS ........................................................................................................................... ix ACRONYMS ............................................................................................................................................. xiii

Online Monitoring of Induction Motors

The online monitoring of active components project, under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program, researched diagnostic and prognostic models for alternating current induction motors (IM). Idaho National Laboratory (INL) worked with the Electric Power Research Institute (EPRI) to augment and revise the fault signatures previously implemented in the Asset Fault Signature Database of EPRI’s Fleet Wide Prognostic and Health Management (FW PHM) Suite software. Induction Motor diagnostic models were researched using the experimental data collected by Idaho State University. Prognostic models were explored in the set of literature and through a limited experiment with 40HP to seek the Remaining Useful Life Database of the FW PHM Suite.

Plan for Demonstration of Online Monitoring for the Light Water Reactor Sustainability Online Monitoring Project

Condition based online monitoring technologies and development of diagnostic and prognostic methodologies have drawn tremendous interest in the nuclear industry. It has become important to identify and resolve problems with structures, systems, and components (SSCs) to ensure plant safety, efficiency, and immunity to accidents in the aging fleet of reactors. The Machine Condition Monitoring (MCM) test bed at INL will be used to demonstrate the effectiveness to advancement in online monitoring, sensors, diagnostic and prognostic technologies on a pilot-scale plant that mimics the hydraulics of a nuclear plant. As part of this research project, INL will research available prognostics architectures and their suitability for deployment in a nuclear power plant. In addition, INL will provide recommendation to improve the existing diagnostic and prognostic architectures based on the experimental analysis performed on the MCM test bed.

Online Monitoring Technical Basis and Analysis Framework for Emergency Diesel Generators - Interim Report for FY 2013

................................................................................................................................................ iii EXECUTIVE SUMMARY .......................................................................................................................... v ACRONYMS ............................................................................................................................................... ix

Data Collection Plan to Populate the Light Water Reactor Sustainability Failure Mode Degradation Library

Interest in implementing advanced Prognostic Health Management (PHM) systems in commercial nuclear power plants (NPPs) has increased rapidly in recent years, with an overarching goal of implementing of improving the safety, reliability, and economics/profitability of the aging nuclear fleet and extending their service life in the most cost-effective manner. The PHM system utilizes prognostic tools to estimate the remaining useful life (RUL) of a component or system of components based on current and predicted operating conditions. An effective implementation of the PHM system will anticipate and identify unique age-dependent degradation modes to provide early warning of emerging problems. Selection of the components and structures to be monitored is a crucial step for successful PHM implementation in NPPs. A selection framework is recommended for risk significant components (both safety-related and non-safety related) based on the Fussell-Vesely (F-V) Importance Measure and the Risk Achievement Worth (RAW) measure. For the selected components, a failure mode degradation library will be developed consisting of data corresponding to different failure/degradation modes. In lieu of constructing an expensive scaled test facility, several data sources are identified for populating the failure mode degradation library, including various national laboratories, universities, agencies, and industries.

Communication Pathways in the Light Water Reactor Sustainability Online Monitoring Project

Implementation of online monitoring and prognostics in existing U.S. nuclear power plants will involve coordinating the efforts of national laboratories, utilities, universities, and private companies. Large amounts of operational data, including failure data, are necessary for the development and calibration of diagnostic and prognostic algorithms. The ability to use data from all available resources will provide the most expeditious avenue to implementation of online monitoring in existing NPPs; however, operational plant data are often considered proprietary. Secure methods for transferring and storing data are discussed, along with a potential technology for implementation of online monitoring.

Establishment of a Hub for the Light Water Reactor Sustainability Online Monitoring Community

Implementation of online monitoring and prognostics in existing U.S. nuclear power plants will involve coordinating the efforts of national laboratories, utilities, universities, and private companies. Internet-based collaborative work environments provide necessary communication tools to facilitate interaction between geographically diverse participants. Available technologies were considered, and a collaborative workspace was established at INL as a hub for the light water reactor sustainability online monitoring community.