Implementation of a Bayesian linear regression framework for nuclear prognostics

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

Implementation of a Bayesian linear regression framework for nuclear prognostics

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

Research output: Contribution to conference › Paper › peer-review

134 Downloads (Pure)

Abstract

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.

Original language	English
Number of pages	9
Publication status	Published - 5 Jul 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/Territory	Spain
City	Bilbao
Period	5/07/16 → 8/07/16

Keywords

asset management
prognostics and health management
Bayesian
steam turbines
nuclear

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Panni-etal-EPHM-2016-Implementation-of-a-Bayesian-linear-regression-frameworkAccepted author manuscript, 1.05 MB

Cite this

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title = "Implementation of a Bayesian linear regression framework for nuclear prognostics",

abstract = "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.",

keywords = "asset management, prognostics and health management, Bayesian, steam turbines , nuclear",

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note = "Third European Conference of the Prognostics and Health Management Society 2016 ; Conference date: 05-07-2016 Through 08-07-2016",

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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 - 2016/7/5

Y1 - 2016/7/5

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

T2 - Third European Conference of the Prognostics and Health Management Society 2016

Y2 - 5 July 2016 through 8 July 2016

ER -

Implementation of a Bayesian linear regression framework for nuclear prognostics

Abstract

Conference

Keywords

Access to Document

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Increasing Certainty of Diagnosis and Prognosis of Plant Health in the Nuclear Industry | Panni, Omer Ahmed Khan

Cite this

Implementation of a Bayesian linear regression framework for nuclear prognostics

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Increasing Certainty of Diagnosis and Prognosis of Plant Health in the Nuclear Industry | Panni, Omer Ahmed Khan

Cite this