TY - JOUR
T1 - Bayesian Network inference for probabilistic strength estimation of aging pipeline systems
AU - Dahire, Sonam
AU - Tahir, Fraaz
AU - Jiao, Yang
AU - Liu, Yongming
N1 - Funding Information:
The work is sponsored by DOT-PHMSA CAAP program (Program Officer: James Prothro and Joshua L. Arnold (current)/James Merritt (former)) and the financial support is greatly appreciated. Technical inputs from Daniel Ersoy and Ernest Lever at Gas Technology Institute (GTI) research experts are highly appreciated as well.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/5
Y1 - 2018/5
N2 - A novel approach for reliability assessment of aging gas pipeline systems based on a Bayesian network methodology is proposed in this paper with a focus on the improvement of the pipeline strength prediction. A multimodal diagnosis is performed by assessing the variation in the mechanical property (e.g., yield strength) within the pipe in terms of material property measurements, such as microstructure, composition, and hardness. The multimodality measurements are then integrated with the Bayesian network information fusion model. Prototype testing is carried out for model verification, validation and demonstration. The model updating scheme employs a Markov Chain Monte Carlo algorithm to infer the posterior distribution of the pipe strength using the multimodality measurements, whereas, the priors are derived from the literature knowledge of such systems. Moreover, through-thickness studies of pipe cross-sections are performed to demonstrate the mechanical property variation from the surface to bulk. Finally, data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. Discussions on the observations and future work are provided.
AB - A novel approach for reliability assessment of aging gas pipeline systems based on a Bayesian network methodology is proposed in this paper with a focus on the improvement of the pipeline strength prediction. A multimodal diagnosis is performed by assessing the variation in the mechanical property (e.g., yield strength) within the pipe in terms of material property measurements, such as microstructure, composition, and hardness. The multimodality measurements are then integrated with the Bayesian network information fusion model. Prototype testing is carried out for model verification, validation and demonstration. The model updating scheme employs a Markov Chain Monte Carlo algorithm to infer the posterior distribution of the pipe strength using the multimodality measurements, whereas, the priors are derived from the literature knowledge of such systems. Moreover, through-thickness studies of pipe cross-sections are performed to demonstrate the mechanical property variation from the surface to bulk. Finally, data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. Discussions on the observations and future work are provided.
KW - Bayesian Network
KW - Inference
KW - Multimodal diagnosis
KW - Probabilistic
KW - Strength
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U2 - 10.1016/j.ijpvp.2018.01.004
DO - 10.1016/j.ijpvp.2018.01.004
M3 - Article
AN - SCOPUS:85042733710
SN - 0308-0161
VL - 162
SP - 30
EP - 39
JO - International Journal of Pressure Vessels and Piping
JF - International Journal of Pressure Vessels and Piping
ER -