Online Monitoring of Plant Assets in the Nuclear Industry
Today’s online monitoring technologies provide opportunities to perform predictive and proactive health management of assets within many different industries, in particular the defense and aerospace industries. The nuclear industry can leverage these technologies to enhance safety, productivity, and reliability of the aging fleet of existing nuclear power plants. The U.S. Department of Energy’s Light Water Reactor Sustainability Program is collaborating with the Electric Power Research Institute’s (EPRI’s) Long- Term Operations program to implement online monitoring in existing nuclear power plants.
Proactive online monitoring in the nuclear industry is being explored using EPRI’s Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software, a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. This paper focuses on development of asset fault signatures used to assess the health status of generator step-up transformers and emergency diesel generators in nuclear power plants. Asset fault signatures describe the distinctive features based on technical examinations that can be used to detect a specific fault type. Fault signatures are developed based on the results of detailed technical research and on the knowledge and experience of technical experts. The Diagnostic Advisor of the FW-PHM Suite software matches developed fault signatures with operational data to provide early identification of critical faults and troubleshooting advice that could be used to distinguish between faults with similar symptoms. This research is important as it will support the automation of predictive online monitoring
How to Cite
Duval, M. (2002). A review of fault detectable by gas-in-oil analysis in transformers. IEEE Electrical Insulation Magazine, vol. 18, no. 3, pp. 8-17.
Electric Power Research Institute (EPRI) (2012). Fleet-Wide Prognostics and Health Management Application Research. Report EPRI 1026712. Electric Power Research Institute,
Electrical Power Research Institute (EPRI) (2012). Asset Fault Signature Requirements. Software manual. Electric Power Research Institute, Charlotte, NC.
Institute of Electrical and Electronics Engineers (IEEE) (2008). IEEE Guide for Interpretation of Gases Generated in Oil-Immersed Transformers. In IEEE, IEEE Std C57.104: 2008. New York, USA.
International Standards Organization (ISO) (1999). Hydraulic fluid power—Fluids—Method for coding the level of contamination by solid particles. In ISO, ISO4406:1999. Genève, Switzerland: International Standards Organization.
Johnson, P. (2012). Fleet wide asset monitoring: Sensory Data to Signal Processing to Prognostics. Proceedings of the Annual Conference of the Prognostics and Health Management Society, September 23-27, Minneapolis, MN. ISBN-978-1-036263-05-9.
Lybeck, N., Agarwal, V., Pham, B., Medema, H., & Fitzgerald, K., (2012). Online Monitoring Technical Basis and Analysis Framework for Large Power Transformers: Interim Report for FY 2012. Report INL/EXT-12-27181. Idaho National Laboratory, Idaho Falls, ID.
Monnin, M., Voisin, A., Leger, J., & Lung, B. (2011). Fleet-Wide Health Management Architecture. Proceedings of the Annual Conference of the Prognostics and Health Management Society, September 25-29, Montreal, Quebec, Canada. ISBN-978-1-936263-03-5.
Monnin, M., Abichou, B., Voisin, A., & Mozzati, C. (2011). Fleet Historical Case for Predictive Maintenance. Proceedings of the International Conference on Surveillance 6, October 25-26, Compiegne, France.
Medina-Oliva, G., Voisin, A., Monnin, M., Peysson, F., & Leger, J. B. (2012). Prognostic Assessment using Fleet- Wide Ontology. Proceedings of the Annual Conference of the Prognostics and Health Management Society, September 23-27, Minneapolis, MN. ISBN-978-1- 036263-05-9.
Patrick, R., Smith, M. J., Byington, C. S., Vachtsevanos, G. J., Tom, K., & Ly, C. (2010). Integrated Software Platform for Fleet Data Analysis, Enhanced Diagnostics, and Safe Transition to Prognostics for Helicopter Component CBM. Proceedings of the Annual Conference of the Prognostics and Health Management Society, October 13-16, Portland, OR. ISBN-978-1-936263-01-1.
Pham, B., Lybeck, N., & Agarwal, V. (2012). Online Monitoring Technical Basis and Analysis Framework for Emergency Diesel Generators: Interim Report for FY 2013. Report INL/EXT-12-27754. Idaho National Laboratory, Idaho Falls, ID.
Umiliacchi, P., Lane, D., & Romano, F. (2011). Predictive Maintenance of Railway Subsystem using an Ontology based Modeling Approach. Proceedings of 9th World Conference on Railway Research, May 22-26, Lille, France.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A Similarity- based Prognostics Approach for Remaining Useful Life Estimation of Engineered Systems. Proceedings of the International Conference on Prognostics and Health Management, October 06-09, Denver, CO.
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