Probabilistic aging pipe strength estimation using multimodality information fusion

Jie Chen, Yongming Liu

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

Abstract

Accurate pipe material strength estimation is critical for the integrity and risk assessment of aging pipeline infrastructure systems. To predict the strength without interrupting the serviceability of the pipeline, inference methods are used through the relationship between the bulk yield tensile strength and surface material properties from nondestructive testing, such as chemical composition, microstructure images, and hardness testing. In order to make the best of information provided by multimodality surface measurements, Bayesian model averaging (BMA) method is used in this paper to integrate the information from various types of surface measurements for a more accurate bulk strength estimation. The models being considered are constructed by randomly combining the multimodality surface measurements and each case of linear combinations is included. The models considered are assessed by assigning different weights 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. To avoid the risk of overfitting, BMA is implemented to account for model uncertainty. The predictive performance of a single model and BMA are compared by logarithmic scoring rule. The data collected from industry are used for demonstration and model predictive performance assessment. It is shown that the Bayesian model averaging approach can provide more reliable results in predicting the strength of the aging 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). Probabilistic aging pipe strength estimation using multimodality information fusion. 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.v11i1.817