On the performance evaluation of a hierarchical-structure prototype product using inconsistent prior information and limited test data

Yang, Lechang; Guo, Yuandong; Kong, Zifan

doi:10.1016/j.ins.2019.02.018

Cited by 22 publications

(13 citation statements)

References 31 publications

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“…NNs are composed of multiple layers, allowing them to learn complex nonlinear relationships. Bayesian deep learning (BDL) and variations thereof have been widely applied to forecast future events given existing data and update when presented with new data [29][30][31][32][33][34][35][36][37]. Deep learning models are only as accurate as the data they are trained on and, as such, typically require large datasets with defined trends over time [37].…”

Section: Multistep Forecasting Methodologiesmentioning

confidence: 99%

Multistep prediction of dynamic uncertainty under limited data

Grenyer

Schwabe

Erkoyuncu

et al. 2022

CIRP Journal of Manufacturing Science and Technology

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Section: Multistep Forecasting Methodologiesmentioning

confidence: 99%

Multistep prediction of dynamic uncertainty under limited data

Grenyer

Schwabe

Erkoyuncu

et al. 2022

CIRP Journal of Manufacturing Science and Technology

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“…how to invert the pooled system lifetime distribution in the system level back to the subsystem level again to update the subsystem distribution. Specific derivations to solve the problem have been given at length in [21], [24]. The result of a continuous form is shown in (2).…”

Section: Background: Bayesian Melding Methodsmentioning

confidence: 99%

“…Guo et al [22] develop the BMM into iterative melding and updating method to assess the system reliability. Yang et al improve the traditional BMM in terms of the pooling weight in [23], [24]. Besides, Guo et al combine heterogeneous information based on BMM by constructing the system structure with Bayesian Networks [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Satellite System Design Optimization Based on Bayesian Melding of Multi-Level Multi-Source Lifetime Information

Yao²,

Zheng

et al. 2019

IEEE Access

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In reliability-based design optimization (RBDO) for the satellite system, reliability modeling is a very important issue. However, due to the limited failure information and small scale production in space engineering, it remains challenging as how to properly construct the reliability model especially in the early conceptual design phase for a new satellite. Actually, there is multi-source lifetime (or failure) information of satellite subsystems or systems from the past missions or designer experiences. How to fully integrate the multi-source information so as to enhance the reliability model with more knowledge is an open problem to be solved. To address this problem, in this paper, it is proposed to use the Bayesian Melding Method (BMM) to merge the multi-source lifetime information from the multi-level structure of satellite system and subsystems, based on which the lifetime distributions can be updated and the satellite reliability model can be properly constructed. With the enhanced reliability model, the satellite RBDO is further studied to optimize the satellite design with budget constraints on mass, cost, and reliability. Finally, two case studies are utilized to demonstrate and verify the proposed method. INDEX TERMS Satellite system design, reliability-based design optimization, Bayesian melding method, multi-source lifetime information.

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“…In Cerqueti et al (2020), there is a specific reference to MCMC for implementing forecasting of the volatility in the environment of financial markets. A list of relevant contributions in the literature highlighting the worthiness of MCMC for applications and for methodological advancements should include e.g., Yang et al (2019); Zanella (2020); Austad (2007); Luengo et al (2020); Mira (2001); Martino (2018). The interested reader is also addressed to the high-level scientific contributions of Diaconis, whose reflections are synthesised in Diaconis (2009Diaconis ( , 2013.…”

Section: Introductionmentioning

confidence: 99%