IDEAS home Printed from https://ideas.repec.org/a/sae/risrel/v236y2022i6p1078-1095.html

A framework for modeling fault propagation paths in air turbine starter based on Bayesian network

Author

Listed:
  • Runxia Guo
  • Zihang Wang

Abstract

Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the improved ant colony optimization algorithm (I-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM), and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, I-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN’s structure. An example of practical application for the propagation path model of typical faults on the A320 air turbine starter is given to verify the validity and feasibility of the proposed method.

Suggested Citation

  • Runxia Guo & Zihang Wang, 2022. "A framework for modeling fault propagation paths in air turbine starter based on Bayesian network," Journal of Risk and Reliability, , vol. 236(6), pages 1078-1095, December.
    • Handle: RePEc:sae:risrel:v:236:y:2022:i:6:p:1078-1095
    DOI: 10.1177/1748006X211052732
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1748006X211052732
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1748006X211052732?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wang, Chaonan & Xing, Liudong & Peng, Rui & Pan, Zhusheng, 2017. "Competing failure analysis in phased-mission systems with multiple functional dependence groups," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 24-33.
    2. Johansson, Jonas & Hassel, Henrik, 2010. "An approach for modelling interdependent infrastructures in the context of vulnerability analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1335-1344.
    3. Rocco S., Claudio M. & Ramirez-Marquez, José Emmanuel, 2011. "Vulnerability metrics and analysis for communities in complex networks," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1360-1366.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Y.F. & Sansavini, G. & Zio, E., 2013. "Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 195-205.
    2. Dui, Hongyan & Meng, Xueyu & Xiao, Hui & Guo, Jianjun, 2020. "Analysis of the cascading failure for scale-free networks based on a multi-strategy evolutionary game," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    3. Zio, Enrico & Piccinelli, Roberta & Delfanti, Maurizio & Olivieri, Valeria & Pozzi, Mauro, 2012. "Application of the load flow and random flow models for the analysis of power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 102-109.
    4. Levitin, Gregory & Finkelstein, Maxim & Dai, Yuanshun, 2020. "Mission abort policy optimization for series systems with overlapping primary and rescue subsystems operating in a random environment," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    5. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    6. Peng, Rui & Wu, Di & Xiao, Hui & Xing, Liudong & Gao, Kaiye, 2019. "Redundancy versus protection for a non-reparable phased-mission system subject to external impacts," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    7. Johnson, Caroline A. & Flage, Roger & Guikema, Seth D., 2021. "Feasibility study of PRA for critical infrastructure risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    8. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    9. Federico Antonello & Piero Baraldi & Enrico Zio & Luigi Serio, 2022. "A Novel Metric to Evaluate the Association Rules for Identification of Functional Dependencies in Complex Technical Infrastructures," Environment Systems and Decisions, Springer, vol. 42(3), pages 436-449, September.
    10. Francis, Royce & Bekera, Behailu, 2014. "A metric and frameworks for resilience analysis of engineered and infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 90-103.
    11. Levitin, Gregory & Finkelstein, Maxim, 2018. "Optimal mission abort policy for systems in a random environment with variable shock rate," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 11-17.
    12. Baroud, Hiba & Barker, Kash & Ramirez-Marquez, Jose E. & Rocco S., Claudio M., 2014. "Importance measures for inland waterway network resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 55-67.
    13. Augutis, Juozas & Jokšas, Benas & Krikštolaitis, Ričardas & Urbonas, Rolandas, 2016. "The assessment technology of energy critical infrastructure," Applied Energy, Elsevier, vol. 162(C), pages 1494-1504.
    14. Heracleous, Constantinos & Kolios, Panayiotis & Panayiotou, Christos G. & Ellinas, Georgios & Polycarpou, Marios M., 2017. "Hybrid systems modeling for critical infrastructures interdependency analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 89-101.
    15. George-Williams, Hindolo & Patelli, Edoardo, 2017. "Efficient availability assessment of reconfigurable multi-state systems with interdependencies," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 431-444.
    16. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    17. Boura, Georgia & Ferguson, Neil S., 2024. "Incorporating geographic interdependencies into the resilience assessment of multimodal public transport networks," Journal of Transport Geography, Elsevier, vol. 118(C).
    18. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    19. Wu, Baichao & Tang, Aiping & Wu, Jie, 2016. "Modeling cascading failures in interdependent infrastructures under terrorist attacks," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 1-8.
    20. Stødle, Kaia & Metcalfe, Caroline A. & Brunner, Logan G. & Saliani, Julian N. & Flage, Roger & Guikema, Seth D., 2021. "Dependent infrastructure system modeling: A case study of the St. Kitts power and water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 209(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:risrel:v:236:y:2022:i:6:p:1078-1095. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.