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Reliability analysis for system by transmitting, pooling and integrating multi-source data

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  • Jia, Xiang
  • Cheng, Zhijun
  • Guo, Bo

Abstract

Reliability analysis of a complex system is vital and challenging. The Bayesian theory is widely used to integrate multiple source data for this problem by decomposing the system into multiple levels. However, the data in the component-level are integrated first and then transmitted to system-level in existing methods. This theory involves extensive computation and is easily affected by the setting of combined weights in the data integration of each level. By contrast, a method is proposed based on multi-source data transmitting first and then pooling together with integration in this paper. Firstly, multi-source data are grouped into lifetime, degradation and other data types and used to determine the corresponding distributions. Next, the data of each data type are separately transmitted to higher level of system. If there are data of identical data type in the higher level, then they are pooled with the transmitted data from the lower level. Finally, all the transmitted data are integrated with the native data in the system-level for reliability analysis of system. An illustrative example shows the application of the proposed method on a system in a satellite. The results, sensitivity analysis, and comparison demonstrate the effectiveness and advantages of the proposed method.

Suggested Citation

  • Jia, Xiang & Cheng, Zhijun & Guo, Bo, 2022. "Reliability analysis for system by transmitting, pooling and integrating multi-source data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:reensy:v:224:y:2022:i:c:s095183202200134x
    DOI: 10.1016/j.ress.2022.108471
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    References listed on IDEAS

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    1. Jiang, R. & Murthy, D.N.P., 2011. "A study of Weibull shape parameter: Properties and significance," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1619-1626.
    2. Jia, Xiang & Nadarajah, Saralees & Guo, Bo, 2018. "The effect of mis-specification on mean and selection between the Weibull and lognormal models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1875-1891.
    3. Mingyang Li & Qingpei Hu & Jian Liu, 2014. "Proportional hazard modeling for hierarchical systems with multi-level information aggregation," IISE Transactions, Taylor & Francis Journals, vol. 46(2), pages 149-163.
    4. Boškoski, Pavle & Debenjak, Andrej & Mileva Boshkoska, Biljana, 2018. "Rayleigh copula for describing impedance data—with application to condition monitoring of proton exchange membrane fuel cells," European Journal of Operational Research, Elsevier, vol. 266(1), pages 269-277.
    5. Wilson, Alyson G. & Anderson-Cook, Christine M. & Huzurbazar, Aparna V., 2011. "A case study for quantifying system reliability and uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1076-1084.
    6. Almalki, Saad J. & Nadarajah, Saralees, 2014. "Modifications of the Weibull distribution: A review," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 32-55.
    7. Castet, Jean-Francois & Saleh, Joseph H., 2009. "Satellite and satellite subsystems reliability: Statistical data analysis and modeling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1718-1728.
    8. Liu, Yingchao & Hu, Xiaofeng & Zhang, Wenjuan, 2019. "Remaining useful life prediction based on health index similarity," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 502-510.
    9. Jia, Xiang & Wang, Dong & Jiang, Ping & Guo, Bo, 2016. "Inference on the reliability of Weibull distribution with multiply Type-I censored data," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 171-181.
    10. Jia, Xiang & Guo, Bo, 2022. "Reliability analysis for complex system with multi-source data integration and multi-level data transmission," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
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