Implementation Of A Bayesian Linear Regression Framework For Nuclear Prognostics



Omer Panni Graeme West Victoria Catterson Stephen McArthur Dongfeng Shi Ieuan Mogridge


Steam turbines are an important asset of nuclear power plants (NPPs), and are required to operate reliably and efficiently. Unplanned outages have a significant impact on the ability of the plant to generate electricity. Therefore, predictive and proactive maintenance which can avoid unplanned outages has the potential to reduce operating costs while increasing the reliability and availability of the plant. A case study from the data of an operational steam turbine of a NPP in the UK was used for the implementation of a Bayesian Linear Regression (BLR) framework. An
appropriate model for the deterioration under study is selected. The BLR framework was applied as a prognostic technique in order to calculate the remaining useful life (RUL). Results show that the accuracy of the technique varies due to the nature of the data that is utilised to estimate the model parameters.

How to Cite

Panni, O., West, G., Catterson, V., McArthur, S., Shi, D., & Mogridge, I. (2016). Implementation Of A Bayesian Linear Regression Framework For Nuclear Prognostics. PHM Society European Conference, 3(1).
Abstract 186 | PDF Downloads 84



Bayesian framework, Nuclear Power Plant Prognostics

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
BSI. (2009). BS ISO 7919-2: Mechanical Vibration - Evaluation Of Machine Vibration By Measurements On Rotating Shafts (Tech. Rep.). British Standards Institute.
Catterson, V. M., Melone, J., & Gracia, M. S. (2016). Prognostics Of Transformer Paper Insulation Using Statistical Particle Filtering Of Online Data. IEEE Electrical Insulation.
Coble, J., Humberstone, M., & Hines, J. W. (2010). Adaptive Monitoring, Fault Detection And Diagnostics, And Prognostics System for The IRIS Nuclear Plant (Tech. Rep.). DTIC Document.
Di Maio, F., Ng, S. S., Tsui, K.-L., & Zio, E. (2011). Na¨ıve Bayesian classifier for on-line remaining useful life prediction of degrading bearings. In MMR2011 (pp. 1–14).
Gebraeel, N. Z., Lawley, M. A., Li, R., & Ryan, J. K. (2005). Residual-Life Distributions From Component Degradation Signals: A Bayesian Approach. IIE Transactions, 37(6), 543–557.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian Data Analysis (2nd ed.). Boca Raton, Florida.
Gu, J., Barker, D., & Pecht, M. (2009). Health Monitoring And Prognostics Of Electronics Subject To Vibration Load Conditions. IEEE Sensors Journal, 9(11), 1479-1485.
Hess, A., & Fila, L. (2002). The Joint Strike Fighter (JSF) PHM Concept: Potential Impact On Aging Aircraft Problems. In (p. 3021 - 3026). IEEE Aerospace Conference.
Kan, M. S., Tan, A. C., & Mathew, J. (2015). A Review On Prognostic Techniques For Non-stationary and Nonlinear Rotating Systems. Mechanical Systems and Signal Processing, 62-63, 1–20.
Killick, R., & Eckley, I. (2014). Changepoint: An R Package For Changepoint Analysis. Journal of Statistical Software, 58(3), 1–19.
Leyzerovich, A. S. (2008). Steam Turbines for Modern Fossil-Fuel Power Plants. Georgia, USA: The Fairmont Press, Inc.
Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Cambridge, MA: The MIT Press.
Qiancheng, W., Shunong, Z., & Rui, K. (2011). Research Of Small Samples Avionics Prognostics Based On Support Vector Machine. In Prognostics and System Health Management Conference, Shenzhen, China (pp. 1–5).
Rudd, S., Catterson, V. M., McArthur, S., & Johnstone, C. (2011). Circuit Breaker Prognostics Using SF6 Circuit Breaker Prognostics Using SF6 Data. IEEE Power and Energy Society General Meeting.
Saha, B., Celaya, J. R., Goebel, K., & Wysocki, P. F. (2009). Towards Prognostics For Electronics Components. In IEEE Aerospace Conference, Montana, USA (p. 1-7).
Sun, B., Zeng, S., Kang, R., & Pecht, M. (2012). Benefits and Challenges of System Prognostics. IEEE Transactions on Reliability, 61(2), 323-335.
Zaidan, M., Mills, A., & Harrison, R. (2013). Bayesian framework for aerospace gas turbine engine prognostics. In IEEE Aerospace Conference, Montana, USA (p. 1-8).
Zheng, Y., Wu, L., Li, X., & Yin, C. (2014). A Relevance Vector Machine-Based Approach For Remaining Useful Life Prediction Of Power MOSFETs. In Prognostics and System Health Management Conference, Hunan, China (p. 642-646).
Technical Papers

Similar Articles

1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.