多通道一维残差卷积神经网络在风力发电机组轴承故障智能诊断中的应用
郑梁1,2,刘桂然*,1,2,朱孝晗1,2
(1.国电联合动力技术有限公司,北京 100039;2.风电设备及控制国家重点实验室,河北 保定 071000)
摘要:为提高卷积神经网络在风力发电机组轴承故障诊断上的准确率,本文对某2 MW风力发电机组轴承故障数据,进行单通道及多通道、多种诊断网络模型、不同优化算法的故障诊断分析对比,提出将多个振动传感器的数据整合为多通道一维数据集,再使用一维残差卷积神经网络进行故障诊断。得出基于Adam优化算法的多通道一维残差卷积神经网络诊断准确率最高。因此,多通道一维残差卷积神经网络在风力发电机组轴承故障诊断中应用效果良好,能够准确的识别各类故障模式,为机组的安全、稳定运行提供了保障。
关键词:风力发电机组;智能故障诊断;多通道数据;一维残差卷积神经网络
中图分类号:TM315 文献标志码:A doi:10.3969/j.issn.1006-0316.2023.03.001
文章编号:1006-0316 (2023) 03-0001-07
Application of Multi-Channel One-Dimension Residual Convolution Neural Network in Intelligent Fault Diagnosis of Wind Turbine Bearing
ZHENG Liang1,2,LIU Guiran1,2,ZHU Xiaohan1,2
( 1.Guodian United Power Technology Co., Ltd., Beijing 100039, China; 2.State Key Laboratory of Wind Power Equipment and Control, Baoding 071000, China )
Abstract:To improve the accuracy of convolution neural network on fault diagnosis of wind turbine bearing,  the bearing fault data of a 2 MW wind turbine generator unit are analyzed and compared with single channel and multiple channels, multiple diagnosis network models  and different optimization algorithm. It is proposed to integrate multiple vibration sensor data for multi-channel one-dimension data set, and then one-dimension residual convolution neural network is used for fault diagnosis. It is concluded that the multi-channel one-dimension residual convolution neural network based on Adam optimization algorithm has the highest diagnostic accuracy. Therefore, multi-channel one-dimension residual convolution neural network has good application effect on failure diagnosis of wind turbine bearing, which can accurately identify various fault modes and provide guarantee for safe and stable operation of the wind turbine. 
Key words:wind turbine;intelligent fault diagnosis;multichannel data;one-dimension residual convolution neural network
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收稿日期:2022-11-04
基金项目:国家重点研发计划(2019YFB2005005-02)
作者简介:郑梁(1981-),男,湖北黄冈人,硕士,工程师,主要研究方向为风力发电机组设计及故障诊断,E-mail:12015018@ceic.com。*通讯作者:刘桂然(1981-),男,河北沧州人,硕士,高级工程师,主要研究方向为风力发电机组轴承设计,E-mail:12079947@ceic.com。
 

 

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