Dual-input convolutional neural network for graphical features based remaining useful life prognosticating of wind turbine bearings

被引：0
|
作者：
Yu P. [1 ,2 ,3 ]
Cao J. [1 ]
机构：
[1] College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou
[2] Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou
[3] National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 05期
关键词：
Dual-input convolutional neural network; Graphical features; Prognosticating; Remaining useful life; Wind turbine bearing;
D O I：
10.19912/j.0254-0096.tynxb.2020-0445
中图分类号：
学科分类号：
摘要：
A graphical features based remaining useful life (RUL) prognosticating method for the bearings in wind turbine is proposed in this paper. Firstly, preprocessing the time-domain vibration data sample set based on continuous wavelet transform (CWT) to obtain the time-frequency graphical data set used for prognosticating work. Secondly, dual-input convolutional neural network (DICNN) is employed to extract the feature map from the graphical data set to construct high performance health indicator (DICNN-HI) for representing the state of each degradation stage of the bearing. Finally, according to the predicted DICNN-HI, a Gaussian process regression (GPR)-based analysis is used for RUL prognosticating, which is verified by the PRONOSTIA ball bearing data set. Results illustrate that the proposed method has a high prediction accuracy of health indicator to map the state of degradation of a bearing effectively, which is helpful to realize the RUL prognosticating accurately in this study. It provides an important theoretical reference for RUL prognosticating of bearing and the other rotating machineries, as well as a certain practical value. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
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页码：343 / 350
页数：7
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