A Dynamic-Bayesian-Networks-Based Resilience Assessment Approach of Structure Systems: Subsea Oil and Gas Pipelines as A Case Study
doi: 10.1007/s13344-020-0054-0
-
Abstract: Under unanticipated natural disasters, any failure of structure components may cause the crash of an entire structure system. Resilience is an important metric for the structure system. Although many resilience metrics and assessment approaches are proposed for engineering system, they are not suitable for complex structure systems, since the failure mechanisms of them are different under the influences of natural disasters. This paper proposes a novel resilience assessment metric for structure system from a macroscopic perspective, named structure resilience, and develops a corresponding assessment approach based on remaining useful life of key components. Dynamic Bayesian networks (DBNs) and Markov are applied to establish the resilience assessment model. In the degradation process, natural degradation and accelerated degradation are modelled by using Bayesian networks, and then coupled by using DBNs. In the recovery process, the model is established by combining Markov and DBNs. Subsea oil and gas pipelines are adopted to demonstrate the application of the proposed structure metric and assessment approach.
-
Figure 2. System state transition laws: (a) two-state transition; (b) three-state transition.
Figure 20. Parameters sensitivity analysis results, (a) 1st hour (b) 20th hour (c) 40th hour.
Table 1. Parameters of the earthquake model
Parameter Distribution Mean Standard deviation D0 (mm) Exponential 0.15 2 M Normal 2.5 0.2 K Lognormal 2.5 0.36 $ {\textit{λ}}$ Deterministic 0.8 − 
下载: 导出CSV
Table 2. Parameters of the corrosion model
Parameter Value Probability
(%)Value Probability
(%)Value Probability
(%)t (°C) 20 30 22.5 40 25 30 U (m/s) 1 20 2 50 3 30 d (mm) 40 23.3 50.8 56.2 65 20.5
下载: 导出CSV
Table 3. Parameters of the sand erosion model
Parameter Value Probability
(%)Value Probability
(%)Value Probability
(%)Vp (m/s) 1 20 2 50 3 30 $ {\textit{θ}}$ (rad) 0.5 30 0.5235 40 0.55 30
下载: 导出CSV
-
[1] Abimbola, M. and Khan, F., 2019. Resilience modeling of engineering systems using dynamic object-oriented Bayesian network approach, Computers & Industrial Engineering, 130, 108–118. [2] Cai, B.P., Liu, Y.H., Liu, Z.K., Tian, X.J., Dong, X. and Yu, S.L., 2012. Using Bayesian networks in reliability evaluation for subsea blowout preventer control system, Reliability Engineering & System Safety, 108, 32–41. [3] Cai, B.P., Shao, X.Y., Liu, Y.H., Kong, X.D., Wang, H.F., Xu, H.Q. and Ge, W.F., 2020. Remaining useful life estimation of structure systems under the influence of multiple causes: Subsea pipelines as a case study, IEEE Transactions on Industrial Electronics, 67(7), 5737–5747. doi: 10.1109/TIE.2019.2931491 [4] Cai, B.P., Xie, M., Liu, Y.H., Liu, Y.L. and Feng, Q., 2018. Availability-based engineering resilience metric and its corresponding evaluation methodology, Reliability Engineering & System Safety, 172, 216–224. [5] De Waard, C., Lotz, U. and Milliams, D.E., 1991. Predictive model for CO2 corrosion engineering in wet natural gas pipelines, Corrosion, 47(12), 976–985. doi: 10.5006/1.3585212 [6] Dhulipala, S.L.N. and Flint, M.M., 2020. Series of semi-Markov processes to model infrastructure resilience under multihazards, Reliability Engineering & System Safety, 193, 106659. [7] Feng, Q., Zhao, X.J., Fan, D.M., Cai, B.P., Liu, Y.Q. and Ren, Y., 2019. Resilience design method based on meta-structure: A case study of offshore wind farm, Reliability Engineering & System Safety, 186, 232–244. [8] Francis, R. and Bekera, B., 2014. A metric and frameworks for resilience analysis of engineered and infrastructure systems, Reliability Engineering & System Safety, 121, 90–103. [9] Fu, J.J. and Khan, F., 2019. Operational failure model for semi-submersible mobile units in harsh environments, Ocean Engineering, 191, 106332. doi: 10.1016/j.oceaneng.2019.106332 [10] Henry, D. and Emmanuel Ramirez-Marquez, J., 2012. Generic metrics and quantitative approaches for system resilience as a function of time, Reliability Engineering & System Safety, 99, 114–122. [11] Hosseini, S. and Barker, K., 2016. Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports, Computers & Industrial Engineering, 93, 252–266. [12] John, A., Yang, Z.L., Riahi, R. and Wang, J., 2016. A risk assessment approach to improve the resilience of a seaport system using Bayesian networks, Ocean Engineering, 111, 136–147. doi: 10.1016/j.oceaneng.2015.10.048 [13] Lin, S.Y. and El-Tawil, S., 2020. Time-dependent resilience assessment of seismic damage and restoration of interdependent lifeline systems, Journal of Infrastructure Systems, 26(1), 04019040. doi: 10.1061/(ASCE)IS.1943-555X.0000522 [14] Luque, J. and Straub, D., 2016. Reliability analysis and updating of deteriorating systems with dynamic Bayesian networks, Structural Safety, 62, 34–46. doi: 10.1016/j.strusafe.2016.03.004 [15] MacKenzie, C.A. and Hu, C., 2019. Decision making under uncertainty for design of resilient engineered systems, Reliability Engineering & System Safety, 192, 106171. [16] Mazumder, R.K., Salman, A.M., Li, Y. and Yu, X., 2020. Seismic functionality and resilience analysis of water distribution systems, Journal of Pipeline Systems Engineering and Practice, 11(1). doi: 10.1061/(ASCE)PS.1949-1204.0000418 [17] Nešić, S., Nordsveen, M., Nyborg, R. and Stangeland, A., 2003. A mechanistic model for carbon dioxide corrosion of mild steel in the presence of protective iron carbonate films - Part 2: A numerical experiment, Corrosion, 59(6), 489–497. doi: 10.5006/1.3277579 [18] Rebello, S., Yu, H.Y. and Ma, L., 2018. An integrated approach for system functional reliability assessment using dynamic Bayesian Network and hidden Markov model, Reliability Engineering & System Safety, 180, 124–135. [19] Shirali, G.A., Mohammadfam, I. and Ebrahimipour, V., 2013. A new method for quantitative assessment of resilience engineering by PCA and NT approach: a case study in a process industry, Reliability Engineering & System Safety, 119, 88–94. [20] Specking, E., Cottam, B., Parnell, G., Pohl, E., Cilli, M., Buchanan, R., Wade, Z. and Small, C., 2019. Assessing engineering resilience for systems with multiple performance measures, Risk Analysis, 39(9), 1899–1912. doi: 10.1111/risa.13395 [21] Straub, D., 2009. Stochastic modeling of deterioration processes through dynamic Bayesian networks, Journal of Engineering Mechanics, 135(10), 1089–1099. doi: 10.1061/(ASCE)EM.1943-7889.0000024 [22] Vieira, R. E., Mansouri, A., McLaury, B.S. and Shirazi S.A., 2016. Experimental and computational study of erosion in elbows due to sand particles in air flow, Powder Technology, 288, 339–353. doi: 10.1016/j.powtec.2015.11.028 [23] Yang, Y.S., Khan, F., Thodi, P. and Abbassi, R., 2017. Corrosion induced failure analysis of subsea pipelines, Reliability Engineering & System Safety, 159, 214–222. [24] Yodo, N., Wang, P.F. and Rafi, M., 2018. Enabling resilience of complex engineered systems using control theory, IEEE Transactions on Reliability, 67(1), 53–65. doi: 10.1109/TR.2017.2746754 [25] Zhang, Y.H., Li, Y.Y. and Kennedy, D., 2019. An uncertain computational model for random vibration analysis of subsea pipelines subjected to spatially varying ground motions, Engineering Structures, 183, 550–561. doi: 10.1016/j.engstruct.2019.01.031 -
下载:
点击查看大图
图(20) / 表(3)
计量
- 文章访问数: 160
- HTML全文浏览量: 107
- PDF下载量: 0
- 被引次数: 0
京公网安备 11010502036328号 京ICP备17033152号