A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines

Xiaoyu Yang; Zhigeng Fang; Yingjie Yang; David Mba; Xiaochuan Li

doi:10.1016/j.apm.2019.02.043

A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines

Xiaoyu Yang^*, Zhigeng Fang, Yingjie Yang, David Mba, Xiaochuan Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

32 Citations (SciVal)

Original language	English
Pages (from-to)	543-557
Number of pages	15
Journal	Applied Mathematical Modelling
Volume	71
DOIs	https://doi.org/10.1016/j.apm.2019.02.043
Publication status	Published (VoR) - Jul 2019

Funding

Thank the editors and the anonymous referees for their insightful comments to improve the paper. This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0233), the China Scholarship fund, the National Social Science Foundation of China (12AZD102), the National Natural Science Foundation of China (No. 71671091 , 71701098 ), the Fundamental Research Funds for Central Universities (NJ20150036), the Natural Science Foundation of Jiangsu Province ( BK20160940 ), the Open Fund of postgraduate Innovation Base (Laboratory) at Nanjing University of Aeronautics and Astronautics ( kfjj20170906 ), the Leverhulme Trust International Research Network project (IN-2014-020) and the Royal Society and NSFC International Exchanges project (IEC\NSFC\170391).

Keywords

GM(1,1)
Information fusion
Non-equidistance
Wear trend
Wind turbine

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10.1016/j.apm.2019.02.043

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title = "A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines",

keywords = "GM(1,1), Information fusion, Non-equidistance, Wear trend, Wind turbine",

author = "Xiaoyu Yang and Zhigeng Fang and Yingjie Yang and David Mba and Xiaochuan Li",

note = "Funding Information: Thank the editors and the anonymous referees for their insightful comments to improve the paper. This work was supported by the Postgraduate Research \& Practice Innovation Program of Jiangsu Province (KYCX18\_0233), the China Scholarship fund, the National Social Science Foundation of China (12AZD102), the National Natural Science Foundation of China (No. 71671091 , 71701098 ), the Fundamental Research Funds for Central Universities (NJ20150036), the Natural Science Foundation of Jiangsu Province ( BK20160940 ), the Open Fund of postgraduate Innovation Base (Laboratory) at Nanjing University of Aeronautics and Astronautics ( kfjj20170906 ), the Leverhulme Trust International Research Network project (IN-2014-020) and the Royal Society and NSFC International Exchanges project (IEC\textbackslash{}NSFC\textbackslash{}170391). Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",

year = "2019",

month = jul,

doi = "10.1016/j.apm.2019.02.043",

language = "English",

volume = "71",

pages = "543--557",

journal = "Applied Mathematical Modelling",

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T1 - A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines

AU - Yang, Xiaoyu

AU - Fang, Zhigeng

AU - Yang, Yingjie

AU - Mba, David

AU - Li, Xiaochuan

N1 - Funding Information: Thank the editors and the anonymous referees for their insightful comments to improve the paper. This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0233), the China Scholarship fund, the National Social Science Foundation of China (12AZD102), the National Natural Science Foundation of China (No. 71671091 , 71701098 ), the Fundamental Research Funds for Central Universities (NJ20150036), the Natural Science Foundation of Jiangsu Province ( BK20160940 ), the Open Fund of postgraduate Innovation Base (Laboratory) at Nanjing University of Aeronautics and Astronautics ( kfjj20170906 ), the Leverhulme Trust International Research Network project (IN-2014-020) and the Royal Society and NSFC International Exchanges project (IEC\NSFC\170391). Publisher Copyright: © 2019 Elsevier Inc.

PY - 2019/7

Y1 - 2019/7

KW - GM(1,1)

KW - Information fusion

KW - Non-equidistance

KW - Wear trend

KW - Wind turbine

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VL - 71

SP - 543

EP - 557

JO - Applied Mathematical Modelling

JF - Applied Mathematical Modelling

ER -

A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines

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