TY - GEN
T1 - Addressing uncertainty in predictive estimates of risk
AU - Bonebrake, C. A.
AU - Ramuhalli, P.
AU - Ivans, W. J.
AU - Coles, G. A.
AU - Hirt, E. H.
PY - 2015
Y1 - 2015
N2 - Probabilistic risk assessment (PRA) provides a static representation of risk associated with operations and maintenance (O&M) of nuclear power plants. Generic aging models can be used to predict the associated risk over time based on the assumed aging characteristics of specific components. These methods do not take into account the current condition of the components, and are susceptible to error in probabilistic estimates. Enhanced risk monitors (ERMs) use component condition (based on condition monitoring data) and time-dependent failure probabilities from prognostic health management (PHM) systems to calculate the risk associated with continued operation using potentially degraded components. ERMs are likely to be of value with advanced reactors, given the relative lack of operational data and the potential need to inform design choices and O&M actions to optimize plant performance, economics, and safety. Using equipment condition assessment methods to gather real-time conditions of advanced reactor components provides more accurate data to input into risk assessments within the ERM framework. This time-dependent condition data can be used as inputs for aging models in order to forecast the probabilistic risk associated with O&M. Such data is subject to uncertainties from measurements and historical operational conditions, along with uncertainties in aging models for components, resulting in uncertainty in estimates of component condition and predicted risk. Periodic updates with real-time measurements may be used to mitigate some uncertainty, which will need to be quantified. This paper describes the basic methodologies for incorporating periodic equipment condition monitoring data into an aging model to provide a forecasted risk assessment for prototypical advanced reactor components. This ERM methodology will include methods for propagating uncertainty over time within a dynamic predictive risk assessment that accounts for multiple interconnected components and failure events. Results of applying the ERM for a simplified advanced reactor PRA model will be presented.
AB - Probabilistic risk assessment (PRA) provides a static representation of risk associated with operations and maintenance (O&M) of nuclear power plants. Generic aging models can be used to predict the associated risk over time based on the assumed aging characteristics of specific components. These methods do not take into account the current condition of the components, and are susceptible to error in probabilistic estimates. Enhanced risk monitors (ERMs) use component condition (based on condition monitoring data) and time-dependent failure probabilities from prognostic health management (PHM) systems to calculate the risk associated with continued operation using potentially degraded components. ERMs are likely to be of value with advanced reactors, given the relative lack of operational data and the potential need to inform design choices and O&M actions to optimize plant performance, economics, and safety. Using equipment condition assessment methods to gather real-time conditions of advanced reactor components provides more accurate data to input into risk assessments within the ERM framework. This time-dependent condition data can be used as inputs for aging models in order to forecast the probabilistic risk associated with O&M. Such data is subject to uncertainties from measurements and historical operational conditions, along with uncertainties in aging models for components, resulting in uncertainty in estimates of component condition and predicted risk. Periodic updates with real-time measurements may be used to mitigate some uncertainty, which will need to be quantified. This paper describes the basic methodologies for incorporating periodic equipment condition monitoring data into an aging model to provide a forecasted risk assessment for prototypical advanced reactor components. This ERM methodology will include methods for propagating uncertainty over time within a dynamic predictive risk assessment that accounts for multiple interconnected components and failure events. Results of applying the ERM for a simplified advanced reactor PRA model will be presented.
KW - Enhanced risk monitors
KW - Equipment condition assessment
KW - Operations and maintenance
KW - Probabilistic risk assessment
KW - Prognostic health management
UR - http://www.scopus.com/inward/record.url?scp=84946143787&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84946143787
T3 - 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2015
SP - 1230
EP - 1238
BT - 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2015
PB - American Nuclear Society
T2 - 9th International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC and HMIT 2015
Y2 - 22 February 2015 through 26 February 2015
ER -