Reliability analysis for complex system with multi-source data integration and multi-level data transmission. Jia, X. & Guo, B. Reliability Engineering & System Safety, 217:108050, January, 2022. ![Reliability analysis for complex system with multi-source data integration and multi-level data transmission [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The Bayesian theory is appealing for reliability analysis of complex system using the data incorporation. However, there are still gaps among existing methods, such as that the common method is applicable to two-level system, multi-source data for components are not considered, k-out-of-n and standby structures are not studied with series and parallel simultaneously, the reliability is usually estimated as discrete variable, etc. For these problems, a Bayesian-based method is proposed on system under multiple levels. First, multi-source data for each target are integrated in lower level by Bayesian theory to derive the posterior distribution for reliability. Next, they are transmitted to higher level through the deterministic function concerning reliability depending on system structure. Further, the transmitted data from lower level are transformed to induced prior distribution for parameters in distribution of lifetime and integrated with native data in higher level to obtain the posterior for reliability. Finally, the reliability and remaining useful lifetime with respect to times in system-level are presented after the successive information propagation. An illustrative example is given to show the application of proposed method. Together with the sensitivity analysis, it proves that this method is feasible, practical and robust.
@article{jia_reliability_2022,
title = {Reliability analysis for complex system with multi-source data integration and multi-level data transmission},
volume = {217},
issn = {0951-8320},
url = {https://www.sciencedirect.com/science/article/pii/S0951832021005536},
doi = {10.1016/j.ress.2021.108050},
abstract = {The Bayesian theory is appealing for reliability analysis of complex system using the data incorporation. However, there are still gaps among existing methods, such as that the common method is applicable to two-level system, multi-source data for components are not considered, k-out-of-n and standby structures are not studied with series and parallel simultaneously, the reliability is usually estimated as discrete variable, etc. For these problems, a Bayesian-based method is proposed on system under multiple levels. First, multi-source data for each target are integrated in lower level by Bayesian theory to derive the posterior distribution for reliability. Next, they are transmitted to higher level through the deterministic function concerning reliability depending on system structure. Further, the transmitted data from lower level are transformed to induced prior distribution for parameters in distribution of lifetime and integrated with native data in higher level to obtain the posterior for reliability. Finally, the reliability and remaining useful lifetime with respect to times in system-level are presented after the successive information propagation. An illustrative example is given to show the application of proposed method. Together with the sensitivity analysis, it proves that this method is feasible, practical and robust.},
language = {en},
urldate = {2021-11-15},
journal = {Reliability Engineering \& System Safety},
author = {Jia, Xiang and Guo, Bo},
month = jan,
year = {2022},
keywords = {Data integration, Data transmission, Multi-level data, Multi-source data, Reliability analysis, System},
pages = {108050},
}
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However, there are still gaps among existing methods, such as that the common method is applicable to two-level system, multi-source data for components are not considered, k-out-of-n and standby structures are not studied with series and parallel simultaneously, the reliability is usually estimated as discrete variable, etc. For these problems, a Bayesian-based method is proposed on system under multiple levels. First, multi-source data for each target are integrated in lower level by Bayesian theory to derive the posterior distribution for reliability. Next, they are transmitted to higher level through the deterministic function concerning reliability depending on system structure. Further, the transmitted data from lower level are transformed to induced prior distribution for parameters in distribution of lifetime and integrated with native data in higher level to obtain the posterior for reliability. 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