IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v195y2020ics0951832019302510.html
   My bibliography  Save this article

Analysis of the reliability of a starter-generator using a dynamic Bayesian network

Author

Listed:
  • Lee, Dooyoul
  • Choi, Dongsu

Abstract

The reliability of a starter-generator in transport aircraft was assessed. Using the process of reliability-centered maintenance (RCM), necessary decisions were made not only to satisfy the reliability requirement but also to reduce the maintenance load. Failure data have indicated that the life of a starter-generator is limited by the reliability of a bearing. The degradation of the bearing was represented by a dynamic Bayesian network (DBN). Parameters were learned by using the EM algorithm given failure data. The DBN model yielded more conservative risk projection than traditional survival analysis due to the limited number of failure data. The DBN model can make up for the lack of data records by knowledge of experts. Using a calibrated model, the time for inspection was determined to maintain reliability over a prescribed amount of time.

Suggested Citation

  • Lee, Dooyoul & Choi, Dongsu, 2020. "Analysis of the reliability of a starter-generator using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s0951832019302510
    DOI: 10.1016/j.ress.2019.106628
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832019302510
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2019.106628?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alkaff, Abdullah, 2021. "Discrete time dynamic reliability modeling for systems with multistate components," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Li, He & Deng, Zhi-Ming & Golilarz, Noorbakhsh Amiri & Guedes Soares, C., 2021. "Reliability analysis of the main drive system of a CNC machine tool including early failures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    3. Zhiqiang Liu & Wenbo Zhu & Hongzhou Zhang & Shengjin Wang & Lu Fang & Weijun Hong & Hua Shao & Guopeng Wang, 2020. "Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network," International Journal of Distributed Sensor Networks, , vol. 16(3), pages 15501477209, March.
    4. Wojciech Wawrzyński & Mariusz Zieja & Justyna Tomaszewska & Mariusz Michalski, 2021. "Reliability Assessment of Aircraft Commutators," Energies, MDPI, vol. 14(21), pages 1-19, November.
    5. Chen, Yinuo & Tian, Zhigang & He, Rui & Wang, Yifei & Xie, Shuyi, 2023. "Discovery of potential risks for the gas transmission station using monitoring data and the OOBN method," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Ding, Feng & Wang, Yihua & Ma, Guoliang & Zhang, Xinrui, 2021. "Correlation reliability assessment of artillery chassis transmission system based on CBN model," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    8. Bahareh Tajiani & Jørn Vatn, 2023. "Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1756-1777, October.
    9. Zhang, Jiusi & Jiang, Yuchen & Li, Xiang & Huo, Mingyi & Luo, Hao & Yin, Shen, 2022. "An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    10. Chuan Wang & Jun Gou & Yingcheng Tian & Hao Jin & Chao Yu & Yupeng Liu & Jiajun Ma & Yong Xia, 2022. "Reliability and availability evaluation of subsea high integrity pressure protection system using stochastic Petri net," Journal of Risk and Reliability, , vol. 236(3), pages 508-521, June.

    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:eee:reensy:v:195:y:2020:i:c:s0951832019302510. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.