Probabilistic Aging Pipe Strength Estimation Using Multimodality Information Fusion

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Published Sep 22, 2019
Jie Chen Yongming Liu

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 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.

How to Cite

Chen, J., & Liu, Y. (2019). Probabilistic Aging Pipe Strength Estimation Using Multimodality Information Fusion. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.817
Abstract 162 | PDF Downloads 181

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Keywords

Aging pipes, Strength estimation, Multimodality information fusion, Bayesian model averaging

Section
Technical Papers

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