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Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details

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  • Lee, Dooyoul
  • Kwon, Kybeom

Abstract

Dynamic Bayesian network (DBN) models are widely used for structural risk analysis because of their powerful parameter learning ability and capability of simplifying the problem by parsing it using nodes and arcs. However, these models update the reliability after inspection and maintenance (I&M) in a manner different than that specified by the aircraft structural integrity program (ASIP). In this study, a DBN model is developed to correctly represent the ASIP method. The model updates the crack length distribution after I&M based on the nondestructive testing (NDT) reliability and repair crack length distribution. The nodes for inequality and equality data are explicitly represented in the DBN, which correspond to the crack length vs. signal amplitude and noise characteristics of the NDT system. The proposed model overcomes drawbacks of existing models—initial overestimation and final underestimation—by appropriately considering the repair crack length distribution. Specifically, a decision node is used, which records the fraction of the crack length distribution removed after inspection. Furthermore, a method for constructing conditional probability tables is presented. The proposed model is applied to the in-service fatigue problem for a J85 engine compressor rotor blade. The findings demonstrate that the proposed model can be used in a wide range of applications.

Suggested Citation

  • Lee, Dooyoul & Kwon, Kybeom, 2023. "Dynamic Bayesian network model for comprehensive risk analysis of fatigue-critical structural details," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004525
    DOI: 10.1016/j.ress.2022.108834
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    References listed on IDEAS

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    1. Bismut, Elizabeth & Straub, Daniel, 2021. "Optimal adaptive inspection and maintenance planning for deteriorating structural systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Zwirglmaier, Kilian & Straub, Daniel, 2016. "A discretization procedure for rare events in Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 96-109.
    3. Jiang, Shan & Li, Yan-Fu, 2021. "Dynamic Reliability Assessment of Multi-cracked Structure under Fatigue Loading via Multi-State Physics Model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Francesco, Domenic Di & Chryssanthopoulos, Marios & Faber, Michael Havbro & Bharadwaj, Ujjwal, 2020. "Consistent and coherent treatment of uncertainties and dependencies in fatigue crack growth calculations using multi-level Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Mendoza, Jorge & Bismut, Elizabeth & Straub, Daniel & Köhler, Jochen, 2022. "Optimal life-cycle mitigation of fatigue failure risk for structural systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    7. Zhu, Jiandao & Collette, Matthew, 2015. "A dynamic discretization method for reliability inference in Dynamic Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 242-252.
    8. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    9. Shittu, Abdulhakim Adeoye & Mehmanparast, Ali & Hart, Phil & Kolios, Athanasios, 2021. "Comparative study between S-N and fracture mechanics approach on reliability assessment of offshore wind turbine jacket foundations," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
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    Cited by:

    1. Hunte, Joshua L. & Neil, Martin & Fenton, Norman E., 2024. "A hybrid Bayesian network for medical device risk assessment and management," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Caetano, Henrique O. & N., Luiz Desuó & Fogliatto, Matheus S.S. & Maciel, Carlos D., 2024. "Resilience assessment of critical infrastructures using dynamic Bayesian networks and evidence propagation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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