Reliability Analysis of Subsea Separation and Boosting System Using Dynamic Bayesian Network

2017 ◽  
Author(s):  
Jiayong Zhu ◽  
Andres Hernandez ◽  
Ankita Taneja ◽  
Bin Zhang ◽  
M. Sam Mannan
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
Dynamic Bayesian Network

scholarly journals Dynamic Reliability Analysis of Corroded Pipeline Using Bayesian Network

2018 ◽  
Vol 7 (4.35) ◽  
pp. 210
Author(s):  
Nurul Sa’aadah Sulaiman ◽  
Henry Tan
Keyword(s):  
Bayesian Network ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Dynamic Bayesian Network ◽  
Operating Pressure ◽  
Dynamic Reliability ◽  
Time Dynamic ◽  
Remaining Service Life ◽  
Reliability Method ◽  
Deterioration Process

Maintenance and integrity management of hydrocarbons pipelines face the challenges from uncertainties in the data available. This paper demonstrates a way for pipeline remaining service life prediction that integrates structural reliability analysis, accumulated corrosion knowledge, and inspection data on a sound mathematical foundation. Pipeline defects depth grows with time according to an empirical corrosion power law, and this is checked for leakage and rupture probability. The pipeline operating pressure is checked with the degraded failure pressure given by ASME B31G code for rupture likelihood. As corrosion process evolves with time, Dynamic Bayesian Network (DBN) is employed to model the stochastic corrosion deterioration process. From the results obtained, the proposed DBN model for pipeline reliability is advanced compared with other traditional structural reliability method whereby the updating ability brings in more accurate prediction results of structural reliability. The comparisons show that the DBN model can achieve a realistic result similar to the conventional method, Monte Carlo Simulation with very minor discrepancy.

Sign in / Sign up

Export Citation Format

Share Document