期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 103, 期 -, 页码 120-135出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2012.03.008
关键词
Ensemble; k-fold cross validation; Weighting schemes; Data-driven prognostics; RUL prediction
资金
- Energy Technology Development Program [2010101010027B]
- International Collaborative R&D Program of Korea Institute of Energy Technology Evaluation and Planning (KETEP) [0420-2011-0161]
- Korean government's Ministry of Knowledge Economy
- National Research Foundation of Korea (NRF) [2011-0022051]
- Korea government
- Basic Research Project of Korea Institute of Machinery and Materials [SC0830]
- Korea Research Council for Industrial Science Technology
- Institute of Advanced Machinery and Design at Seoul National University (SNU-IAMD)
- Korea Evaluation Institute of Industrial Technology (KEIT) [20118520020010] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Council of Science & Technology (NST), Republic of Korea [SC0830] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2011-0022051] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Office Of The Director
- Office of Integrative Activities [903806] Funding Source: National Science Foundation
Prognostics aims at determining whether a failure of an engineered system (e.g., a nuclear power plant) is impending and estimating the remaining useful life (RUL) before the failure occurs. The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust; (ii) it wastes the resources for constructing the algorithms that are discarded; (iii) it requires the testing data in addition to the training data. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. The results obtained from three case studies suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm when member algorithms producing diverse RUL predictions have comparable prediction accuracy and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes. (c) 2012 Elsevier Ltd. All rights reserved.
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