基于图形特征的双输入卷积神经网络风力机轴承剩余寿命预测  

DUAL-INPUT CONVOLUTIONAL NEURAL NETWORK FOR GRAPHICAL FEATURES BASED REMAINING USEFUL LIFE PROGNOSTICATING OF WIND TURBINE BEARINGS

作　　者：余萍[1,2,3] 曹洁 Yu Ping;Cao Jie(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou 730050,China;National Demonstration Center for Experimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou 730050,China Received:2020-05-18 Online:2022-05-28 Published:2022-11-28)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]甘肃省工业过程控制重点实验室,兰州730050 [3]兰州理工大学电气与控制工程国家级实验教学示范中心,兰州730050

出　　处：《太阳能学报》2022年第5期343-350,共8页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(61763028,61563032,61963025);甘肃省自然科学基金(1506RJZA104)。

摘　　要：提出一种基于图形特征的风力机轴承剩余使用寿命(RUL)预测方法。首先,基于连续小波变换(CWT)对时域振动数据样本集进行预处理,得到用于预测的时频图形数据集。然后,采用双输入卷积神经网络(DICNN)从图形数据集中提取特征映射,用于构造高性能健康指数(DICNN-HI)来表征轴承各退化阶段的状态。最后,结合DICNN-HI,采用基于高斯过程回归(GPR)的分析方法进行RUL预测,并用PRONOSTIA滚动轴承数据集进行验证。结果表明,该方法具有较高的健康指数预测精度,能有效反映滚动轴承的劣化状态,有助于实现风力机轴承的RUL预测。同时,也可为其他旋转机械设备的剩余寿命预测提供重要的理论参考,具有一定的实用价值。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.

关 键 词：风力机轴承 双输入卷积神经网络 图形特征 剩余使用寿命 预测 

分 类 号：TG156[金属学及工艺—热处理]