TY - GEN
T1 - Multimodality information fusion for aging pipe strength and toughness estimationusing bayesian networks
AU - Chen, Jie
AU - Liu, Yongming
N1 - Publisher Copyright:
© 2019 Prognostics and Health Management Society. All rights reserved.
PY - 2019/9/23
Y1 - 2019/9/23
N2 - Accurate estimation of the mechanical property of aging pipes is critical to maintain the safety and to scheduling maintenance. Destructive testing for mechanical properties measurement is very expensive and sometime impossible. Inference methods are needed for estimating the bulk properties by multimodality surface material measurements from nondestructive testing, such as chemical composition, volume fraction and hardness. Bayesian network modeling is utilized to integrate the information from various types of surface measurements for a more accurate bulk mechanical property estimation. To improve the approximation of the actual underlying model and avoid the risk of overfitting, Bayesian model averaging (BMA) of Bayesian networks is implemented to account for Bayesian network model uncertainty. The models considered are weighted based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. The predictive performance of single best model and BMA are compared by logarithmic scoring rule. The predictive capability of the proposed method is evaluated. It is shown that the Bayesian network and model averaging approach can provide more reliable results in predicting the bulk mechanical properties of the pipelines.
AB - Accurate estimation of the mechanical property of aging pipes is critical to maintain the safety and to scheduling maintenance. Destructive testing for mechanical properties measurement is very expensive and sometime impossible. Inference methods are needed for estimating the bulk properties by multimodality surface material measurements from nondestructive testing, such as chemical composition, volume fraction and hardness. Bayesian network modeling is utilized to integrate the information from various types of surface measurements for a more accurate bulk mechanical property estimation. To improve the approximation of the actual underlying model and avoid the risk of overfitting, Bayesian model averaging (BMA) of Bayesian networks is implemented to account for Bayesian network model uncertainty. The models considered are weighted based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. The predictive performance of single best model and BMA are compared by logarithmic scoring rule. The predictive capability of the proposed method is evaluated. It is shown that the Bayesian network and model averaging approach can provide more reliable results in predicting the bulk mechanical properties of the pipelines.
UR - http://www.scopus.com/inward/record.url?scp=85083972172&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083972172&partnerID=8YFLogxK
U2 - 10.36001/phmconf.2019.v11i1.917
DO - 10.36001/phmconf.2019.v11i1.917
M3 - Conference contribution
AN - SCOPUS:85083972172
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
A2 - Clements, N. Scott
A2 - Zhang, Bin
A2 - Saxena, Abhinav
PB - Prognostics and Health Management Society
T2 - 11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
Y2 - 23 September 2019 through 26 September 2019
ER -