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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (6): 1212-1219    DOI: 10.3785/j.issn.1008-973X.2022.06.020
能源与机械工程     
基于小波变换和优化CNN的风电齿轮箱故障诊断
温竹鹏1(),陈捷1,2,*(),刘连华1,焦玲玲1
1. 南京工业大学 机械与动力工程学院，江苏 南京 211816
2. 江苏省工业装备数字制造及控制技术重点实验室，江苏 南京 211816
Fault diagnosis of wind power gearbox based on wavelet transform and improved CNN
Zhu-peng WEN1(),Jie CHEN1,2,*(),Lian-hua LIU1,Ling-ling JIAO1
1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China
2. Jiangsu Key Laboratory of Digital Manufacturing for Industrial Equipment and Control Technology, Nanjing 211816, China
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摘要：

针对传统故障诊断方法过于依赖人为经验的缺陷，提出小波变换和二维密集连接扩张卷积神经网络(WT-ICNN)的风电齿轮箱智能故障诊断方法. 所提方法将一维振动信号通过连续小波变换(WT)转换成二维故障图像；再将二维故障图像输入ICNN中进行训练和测试. 通过齿轮箱开源数据和风场实测数据验证结果表明，与传统故障诊断方法相比，所提方法采用密集连接的结构自适应特征提取时频图，有效加强了故障特征的利用效率；在对风电齿轮箱的故障诊断中，所提方法具有更好的特征复用能力和更高的诊断精度.
关键词： 风电齿轮箱;  小波变换;  卷积神经网络;  密集连接;  扩张卷积    
Abstract:

An intelligent fault diagnosis method for wind turbine gearbox based on wavelet transform and two-dimensional densely connected dilated convolutional neural network(WT-ICNN) was proposed, aiming at the problem that traditional fault diagnosis method dependent on human experience too much. One dimensional vibration signal was transformed into two-dimensional fault image by continuous wavelet transform. Then the two-dimensional fault image was inputted into ICNN for training and testing. The verification of open source data of gearbox and measured data of wind field showed that compared with the traditional fault diagnosis methods, the proposed method effectively enhanced the utilization efficiency of fault features by using the densely connected structure for adaptive feature extraction of time-frequency map. And in the fault diagnosis of wind power gearbox, the proposed method had better feature reuse ability and higher diagnosis accuracy.
Key words: wind power gearbox    wavelet transform    convolutional neural network    densely connect    dilated convolution
收稿日期: 2021-06-25 出版日期: 2022-06-30
CLC:  TH 132  
基金资助: 国家重点研发计划资助项目(2019YFB2005005)
通讯作者: 陈捷     E-mail: 1393532653@qq.com;njtechchenjie@163.com
作者简介: 温竹鹏（1996—），男，硕士生，从事齿轮箱故障诊断研究. orcid.org/0000-0003-0160-3113. E-mail： 1393532653@qq.com
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引用本文:

温竹鹏,陈捷,刘连华,焦玲玲. 基于小波变换和优化CNN的风电齿轮箱故障诊断[J]. 浙江大学学报(工学版), 2022, 56(6): 1212-1219.

Zhu-peng WEN,Jie CHEN,Lian-hua LIU,Ling-ling JIAO. Fault diagnosis of wind power gearbox based on wavelet transform and improved CNN. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1212-1219.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.06.020    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I6/1212

图 1  密集连接的结构
图 2  扩张卷积结构
图 3  密集连接扩张卷积神经网络的诊断模型
图 4  动力传动实验模拟平台 [ 14]
图 5  案例1中5种状态的小波时频图
图 6  故障状态训练结果
图 7  案例1分类结果的混淆矩阵
图 8  案例2风电齿轮箱的4类故障
图 9  案例2中5种状态的小波时频图
图 10  案例2分类结果的混淆矩阵
图 11  案例2中5种状态的输入层特征可视化
图 12  案例2中5种状态的Softmax层特征可视化
实验 Acc N Acc Y 实验 Acc N Acc Y
%
1 94.6 99.8 4 93.7 99.6
2 95.7 99.5 5 92.8 99.5
3 94.4 99.3 平均 94.2 99.5
表 1  密集连接对模型分类精度的影响
实验 Acc/% t'/s
N Y N Y
1 99.3 100 261 224
2 98.9 99.8 251 227
3 99.1 99.6 253 228
4 99.1 99.8 247 225
5 99.6 98.2 246 226
平均 99.2 99.5 252 226
表 2  扩张卷积对模型训练效果的影响
模型 $ \eta $ Acc/% 模型 $ \eta $ Acc/%
1DCNN 10 96.0 LSSVM 10 91.6
2DCNN 10 96.7 WT-ICNN 10 99.3
KELM 10 95.7 — — —
表 3  不同模型的诊断精度对比
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