Journal
APPLIED MATHEMATICAL MODELLING
Volume 71, Issue -, Pages 543-557Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.apm.2019.02.043
Keywords
Information fusion; GM(1,1); Non-equidistance; Wear trend; Wind turbine
Funding
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX18_0233]
- China Scholarship fund
- National Social Science Foundation of China [12AZD102]
- National Natural Science Foundation of China [71671091, 71701098]
- Fundamental Research Funds for Central Universities [NJ20150036]
- Natural Science Foundation of Jiangsu Province [BK20160940]
- Open Fund of postgraduate Innovation Base (Laboratory) at Nanjing University of Aeronautics and Astronautics [kfjj20170906]
- Leverhulme Trust International Research Network project [IN-2014-020]
- Royal Society
- NSFC International Exchanges project [IEC\NSFC\170391]
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The small and fluctuating samples of lubricating oil data render the wear trend prediction a challenging task in operation and maintenance management of wind turbine gearboxes. To deal with this problem, this paper puts forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multi-source information into traditional grey models. Multi-source information is applied by creating a mapping sequence according to the sequence to be predicted. The significance of the key parameters in the proposed model was investigated by numerical experiments. Based on the results from the numerical experiments, the effectiveness of the proposed method was demonstrated using lubricating oil data captured from industrial wind turbine gearboxes. A comparative analysis was also conducted with a number of selected other models to illustrate the superiority of the proposed model in dealing with small and fluctuating data. Prediction results show that the proposed model is able to relax the quasi-smooth requirement of data sequence and is much more robust in comparison to exponential regression, linear regression and non-equidistance GM(1,1) models. (C) 2019 Elsevier Inc. All rights reserved.
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