发电技术2023，Vol.44Issue(6)：824-832,9.DOI:10.12096/j.2096-4528.pgt.22178
基于重采样降噪与主成分分析的宽卷积深度神经网络风机故障诊断方法
Fault Diagnosis Method of Wind Turbines Based on Wide Deep Convolutional Neural Network With Resampling and Principal Component Analysis
摘要
Abstract
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.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Key words
wind turbine/wide deep convolutional neural network/resampling/wavelet threshold denoising/principal component analysis分类
能源科技引用本文复制引用
刘展,包琰洋,李大字..基于重采样降噪与主成分分析的宽卷积深度神经网络风机故障诊断方法[J].发电技术,2023,44(6):824-832,9.基金项目
国家自然科学基金项目(62273026) （62273026）
工信部高技术船舶科研项目(MC-202025-S02). Project Supported by National Natural Science Foundation of China(62273026) （MC-202025-S02）
High-Tech Ship Research Project of Ministry of Industry and Information Technology(MC-202025-S02). （MC-202025-S02）
