Abstract
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.
| Language | English |
|---|---|
| Number of pages | 9 |
| Publication status | Accepted/In press - 16 May 2016 |
| Event | Third European Conference of the Prognostics and Health Management Society 2016 - Bilbao, Spain Duration: 5 Jul 2016 → 8 Jul 2016 |
Conference
| Conference | Third European Conference of the Prognostics and Health Management Society 2016 |
|---|---|
| Country | Spain |
| City | Bilbao |
| Period | 5/07/16 → 8/07/16 |
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Keywords
- asset management
- prognostics and health management
- Bayesian
- steam turbines
- nuclear
Cite this
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Implementation of a Bayesian linear regression framework for nuclear prognostics. / Panni, Omer; West, Graeme; Catterson, Victoria; McArthur, Stephen; Shi, Dongfeng; Mogridge, Ieuan.
2016. Paper presented at Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain.Research output: Contribution to conference › Paper
TY - CONF
T1 - Implementation of a Bayesian linear regression framework for nuclear prognostics
AU - Panni, Omer
AU - West, Graeme
AU - Catterson, Victoria
AU - McArthur, Stephen
AU - Shi, Dongfeng
AU - Mogridge, Ieuan
PY - 2016/5/16
Y1 - 2016/5/16
N2 - 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.
AB - 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.
KW - asset management
KW - prognostics and health management
KW - Bayesian
KW - steam turbines
KW - nuclear
UR - http://www.phmsociety.org/events/conference/phm/europe/16
M3 - Paper
ER -