TY - GEN
T1 - Progress towards prognostic health management of passive components in advanced reactors-Model selection and evaluation
AU - Roy, S.
AU - Dib, G.
AU - Ramuhalli, P.
AU - Hirt, E. H.
AU - Prowant, M. S.
AU - Luzi, L.
AU - Pardini, A. F.
AU - Pitman, S. G.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/8
Y1 - 2015/9/8
N2 - This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.
AB - This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.
KW - Bayesian model selection
KW - advanced reactor prognostics
KW - high-temperature creep
KW - particle filter
KW - prognostic health management
KW - reversible jump MCMC
UR - http://www.scopus.com/inward/record.url?scp=84957916687&partnerID=8YFLogxK
U2 - 10.1109/ICPHM.2015.7245059
DO - 10.1109/ICPHM.2015.7245059
M3 - Conference contribution
AN - SCOPUS:84957916687
T3 - 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application, PHM 2015
BT - 2015 IEEE Conference on Prognostics and Health Management
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Conference on Prognostics and Health Management, PHM 2015
Y2 - 22 June 2015 through 25 June 2015
ER -