摘要
针对数据驱动的风机故障诊断面临的数据量少、信号噪声干扰等问题,提出了一种基于宽卷积深度神经网络的故障诊断方法。该方法采用了重采样、小波阈值去噪等信号预处理方式,既增加了信息密度,又保证了信息的完整性,结合主成分分析法(principal component analysis,PCA)替代人工经验进行数据通道的选取。利用卷积神经网络的强大特征提取能力,通过较少的数据训练即可对风机机组在时域上的故障信号进行有效的特征提取,从而可以对风机进行精确的故障诊断。基于某真实风机机组数据的实验结果,验证了该方法的有效性。
Fault diagnosis of wind turbines suffers from less training data and noises.A method based on wide deep convolutional neural network with resampling and principal component analysis was presented for the diagnosis of mechanical faults(that is the main fault component of wind turbines).The method adopted a variety of signal preprocessing methods such as resampling wavelet threshold denoising and principal component analysis to increase the information density and ensure the integrity of the information.After being trained with small amount of data,the network which has a powerful feature extraction capability could extract the fault signal in the time domain which will be further used for fault diagnosis.Experimental results were verified based on the real wind turbine data,demonstrating the effectiveness of this method.
作者
刘展
包琰洋
李大字
LIU Zhan;BAO Yanyang;LI Dazi(Beijing Pukang Automation Technology Co.,Ltd.,Fengtai District,Beijing 100070,China;Institute of Automation,Beijing University of Chemical Technology,Chaoyang District,Beijing 100029,China)
出处
《发电技术》
CSCD
2023年第6期824-832,共9页
Power Generation Technology
基金
国家自然科学基金项目(62273026)
工信部高技术船舶科研项目(MC-202025-S02)。
关键词
风机
宽卷积深度卷积神经网络
重采样
小波阈值去噪
主成分分析法
wind turbine
wide deep convolutional neural network
resampling
wavelet threshold denoising
principal component analysis
