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RISS 인기검색어
- 검색키워드
- 저자 : Weiwen Peng (검색결과 2 건)
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A Bayesian optimal design for degradation tests based on the inverse Gaussian process
Weiwen Peng,Yu Liu,Yanfeng Li,Shun-Peng Zhu,Hong-Zhong Huang 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.10
The inverse Gaussian process is recently introduced as an attractive and flexible stochastic process for degradation modeling. This processhas been demonstrated as a valuable complement for models that are developed on the basis of the Wiener and gamma processes. We investigate the optimal design of the degradation tests on the basis of the inverse Gaussian process. In addition to an optimal designwith pre-estimated planning values of model parameters, we also address the issue of uncertainty in the planning values by using theBayesian method. An average pre-posterior variance of reliability is used as the optimization criterion. A trade-off between sample sizeand number of degradation observations is investigated in the degradation test planning. The effects of priors on the optimal designs andon the value of prior information are also investigated and quantified. The degradation test planning of a GaAs Laser device is performedto demonstrate the proposed method.
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Improved trajectory similarity-based approach for turbofan engine prognostics
Cheng-Geng Huang,Hong-Zhong Huang,Weiwen Peng,Tudi Huang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.10
Trajectory similarity-based prediction (TSBP) is an emerging real-time remaining useful life (RUL) prediction method that has drawn considerable attention in the field of data-driven prognostics. TSBP is fast, and the corresponding model is easy to train. However, TBSP only provides a point estimation of RUL, which is insufficient for some specific prognostic applications. Hence, this study introduces an improved TSBP method to handle the issue of prognostic uncertainty. On the basis of an adaptive kernel density estimation technique and β-criterion, the improved TSBP method not only provides an accurate and precise point prediction of RUL but also specifies the confidence interval of RUL prediction. The capability of obtaining the confidence interval of RUL can enhance the TSBP method for uncertainty management. The effectiveness of the proposed method is validated through two cases studies, which are related to turbofan engine prognostics.
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