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

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  • 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
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    Citations

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    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. 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.
    3. Wojciech Wawrzyński & Mariusz Zieja & Justyna Tomaszewska & Mariusz Michalski, 2021. "Reliability Assessment of Aircraft Commutators," Energies, MDPI, vol. 14(21), pages 1-19, November.
    4. 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.
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    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.

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