EWMA图表
方位(导航)
风力发电
支持向量机
控制图
深信不疑网络
涡轮机
人工神经网络
计算机科学
人工智能
工程类
数据挖掘
机械工程
过程(计算)
操作系统
电气工程
作者
Huajin Li,Jiahao Deng,Shuang Yuan,Peng Feng,Dimuthu D. K. Arachchige
标识
DOI:10.3389/fenrg.2021.799039
摘要
Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.
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