Multimodality information fusion for aging pipe strength and toughness estimationusing bayesian networks

Jie Chen, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
EditorsN. Scott Clements, Bin Zhang, Abhinav Saxena
PublisherPrognostics and Health Management Society
Edition1
ISBN (Electronic)9781936263059
DOIs
StatePublished - Sep 23 2019
Event11th Annual Conference of the Prognostics and Health Management Society, PHM 2019 - Scottsdale, United States
Duration: Sep 23 2019Sep 26 2019

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Number1
Volume11
ISSN (Print)2325-0178

Conference

Conference11th Annual Conference of the Prognostics and Health Management Society, PHM 2019
CountryUnited States
CityScottsdale
Period9/23/199/26/19

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

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  • Cite this

    Chen, J., & Liu, Y. (2019). Multimodality information fusion for aging pipe strength and toughness estimationusing bayesian networks. In N. S. Clements, B. Zhang, & A. Saxena (Eds.), Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (1 ed.). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM; Vol. 11, No. 1). Prognostics and Health Management Society. https://doi.org/10.36001/phmconf.2019.v11i1.917