基于动态加权密集连接卷积网络的变转速行星齿轮箱故障诊断

doi:10.3901/JME.2019.07.052

机械工程学报 ›› 2019, Vol. 55 ›› Issue (7): 52-57.doi: 10.3901/JME.2019.07.052

• 基于深度学习的机械装备故障预测与健康管理 • 上一篇下一篇

基于动态加权密集连接卷积网络的变转速行星齿轮箱故障诊断

熊鹏, 汤宝平, 邓蕾, 赵明航

重庆大学机械传动国家重点实验室重庆 400044

收稿日期:2018-07-09 修回日期:2018-12-21 出版日期:2019-04-05 发布日期:2019-04-05
通讯作者: 汤宝平(通信作者),男,1971年出生,博士,教授,博士研究生导师。主要研究方向为机电装备安全服役与寿命预测、测试计量技术及仪器、无线传感器网络等。E-mail:bptang@cqu.edu.cn
作者简介:熊鹏,男,1989年出生,博士研究生。主要研究方向为机械故障识别。E-mail:davidxiongpeng@gmail.com
基金资助:
国家自然科学基金资助项目（51775065，51675067）。

Fault Diagnosis for Planetary Gearbox by Dynamically Weighted Densely Connected Convolutional Networks

XIONG Peng, TANG Baoping, DENG Lei, ZHAO Minghang

The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044

Received:2018-07-09 Revised:2018-12-21 Online:2019-04-05 Published:2019-04-05

摘要/Abstract

摘要： 针对变转速工况下基于深度学习的行星齿轮箱故障诊断问题，提出动态加权密集连接卷积网络的故障诊断方法。将行星齿轮箱振动信号的小波包系数二维矩阵输入到密集连接卷积网络作为网络的初始特征图；在密集连接卷积网络的跨层连接中加入动态加权层，形成动态加权密集连接卷积网络，加强网络的深层信息传递；通过动态加权网络层自适应提取不同频带内的故障特征信息进行行星齿轮箱故障诊断。试验表明了所提的动态加权密集连接卷积网络能有效诊断变转速行星齿轮箱故障。

关键词: 故障诊断, 密集连接卷积网络, 特征学习, 小波包变换, 行星齿轮箱

Abstract: Aiming at applying deep learning in fault diagnosis of the planetary gearbox, a fault diagnosis method based on dynamically weighted densely connected convolutional networks is proposed. The wavelet packet coefficient matrix of the planetary gearbox vibration signal is taken as the initial feature map for densely connected convolutional networks. Dynamically weighted layers are designed in cross-layer of densely connected convolutional networks to form dynamically weighted densely connected convolutional networks to enhance information flow in deep network layers. The fault features are extracted by dynamically weighted network layers adaptively to perform the planetary gearbox fault diagnosis. The experiment indicates that the dynamically weighted densely connected convolutional networks can realize fault diagnosis of planetary gearbox under varying speed condition more effectively.

Key words: densely connected convolutional networks, fault diagnosis, feature learning, planetary gearbox, wavelet packet transform

中图分类号:

TH17

熊鹏, 汤宝平, 邓蕾, 赵明航. 基于动态加权密集连接卷积网络的变转速行星齿轮箱故障诊断[J]. 机械工程学报, 2019, 55(7): 52-57.

XIONG Peng, TANG Baoping, DENG Lei, ZHAO Minghang. Fault Diagnosis for Planetary Gearbox by Dynamically Weighted Densely Connected Convolutional Networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 52-57.

参考文献

[1] 雷亚国,何正嘉,林京,等. 行星齿轮箱故障诊技术的研究进展[J]. 机械工程学报, 2011, 47(19):59-67. LEI Yaguo, HE Zhengjia, LIN Jing, et al. Research advances of fault diagnosis technique for planetary gearboxes[J]. Journal of Mechanical Engineering, 2011, 47(19):59-67.
[2] LEI Yaguo, LIN Jing, ZUO M J, et al. Condition monitoring and fault diagnosis of planetary gearboxes:A review[J]. Measurement, 2014, 48(1):292-305.
[3] 何国林,丁康,林慧斌. 基于匹配追踪的齿轮箱耦合调制振动信号分离方法研究[J]. 机械工程学报, 2016, 52(1):102-108. HE Guolin, DING Kang, LIN Huibin. Matching pursuit method for coupling modulation signal separation of gearbox vibration[J]. Journal of Mechanical Engineering, 2016, 51(1):102-108.
[4] YU Jun, HE Yongjun. Planetary gearbox fault diagnosis based on data-driven valued characteristic multigranulation model with incomplete diagnostic information[J]. Journal of Sound and Vibration, 2018, 429:63-77.
[5] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436.
[6] JIA Feng, LEI Yaguo, LIN Jing, et al. Deep neural networks:A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems & Signal Processing, 2016, 72-73:303-315.
[7] HE Miao, HE D. Deep Learning based approach for bearing fault diagnosis[J]. IEEE Transactions on Industry Applications, 2017, 53(3):3057-3065.
[8] WANG Lihua, ZHAO Xiaoping, WU Jiaxin, et al. Motor fault diagnosis based on short-time fourier transform and convolutional neural network[J]. Chinese Journal of Mechanical Engineering, 2017, 30(6):1-12.
[9] SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Training very deep networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems(NIPS), December 7-12, 2015, Montreal, Canada. Cambridge, MA, USA:MIT Press, 2015:2377-2385.
[10] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada, United States, 2016:770-778.
[11] ZHAO Minghang, KANG M, TANG Baoping, et al. Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5):4290-4300.
[12] HUANG Gao, LIU Zhuang, WEINBERGER K Q, et al. Densely connected convolutional networks[C]//30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, USA 2017:2261-2269.
[13] WALNUT D. F. An introduction to wavelet analysis[M]. Boston, MA, USA:Birkhäuser, 2004.
[14] YAN Ruqiang, GAO R X, CHEN Xuefeng. Wavelets for fault diagnosis of rotary machines:A review with applications[J]. Signal Processing, 2014, 96(5):1-15.
[15] WANG Yi, XU Guanghua, LIANG Lin, et al. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis[J]. Mechanical Systems & Signal Processing, 2015, 54-55:259-276.
[16] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers:Surpassing human-level performance on ImageNet classification[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Dec.13-16, 2015, Vis, Santiago, Chile, Washington, DC, USA:IEEE Computer Society, 2015:1026-1034.
[17] GOODFELLOW I, BENGIO Y, COURVILLE A, et al. Deep learning[M]. Cambridge:MIT Press, 2016.

基于动态加权密集连接卷积网络的变转速行星齿轮箱故障诊断

Fault Diagnosis for Planetary Gearbox by Dynamically Weighted Densely Connected Convolutional Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	沈长青, 汤盛浩, 江星星, 石娟娟, 王俊, 朱忠奎. 独立自适应学习率优化深度信念网络在轴承故障诊断中的应用研究[J]. 机械工程学报, 2019, 55(7): 81-88.
[2]	王奉涛, 刘晓飞, 敦泊森, 邓刚, 韩清凯, 李宏坤. 基于萤火虫优化的核自动编码器在中介轴承故障诊断中的应用[J]. 机械工程学报, 2019, 55(7): 58-64.
[3]	姜洪开, 邵海东, 李兴球. 基于深度学习的飞行器智能故障诊断方法[J]. 机械工程学报, 2019, 55(7): 27-34.
[4]	胡茑庆, 陈徽鹏, 程哲, 张伦, 张宇. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报, 2019, 55(7): 9-18.
[5]	李川, 张绍辉, José Valente de Oliveira. 基于次优网络深度学习的3D打印机故障诊断[J]. 机械工程学报, 2019, 55(7): 73-80.
[6]	汪久根, 柯梁亮. 基于残差网络的RV减速器故障诊断[J]. 机械工程学报, 2019, 55(3): 73-80.
[7]	翁军华,张剑锐,李达,郑海兴. 光伏系统直流电弧故障检测的抗干扰技术综述[J]. 电气工程学报, 2019, 14(3): 9-15.
[8]	程礼, 杨武奎, 梁涛, 文璧, 姚东野. 基于声模态的压气机/风扇气路故障诊断[J]. 机械工程学报, 2019, 55(13): 38-44.
[9]	陈雪峰, 王诗彬, 程礼. 航空发动机快变信号的匹配同步压缩变换研究[J]. 机械工程学报, 2019, 55(13): 13-22.
[10]	胡超凡, 王衍学. 基于张量分解的滚动轴承复合故障多通道信号降噪方法研究[J]. 机械工程学报, 2019, 55(12): 50-57.
[11]	彭丹丹, 刘志亮, 靳亚强, 秦勇. 基于软筛分停止准则的改进经验模态分解及其在旋转机械故障诊断中的应用[J]. 机械工程学报, 2019, 55(10): 122-132.
[12]	姚德臣, 杨建伟, 程晓卿, 王兴. 基于多尺度本征模态排列熵和SA-SVM的轴承故障诊断研究[J]. 机械工程学报, 2018, 54(9): 168-176.
[13]	宗鸣,冯超,郑安琪. 基于FPGA自适应滤波器的磁轴承位移传感器故障信号处理分析[J]. 电气工程学报, 2018, 13(8): 1-6.
[14]	侯文擎, 叶鸣, 李巍华. 基于改进堆叠降噪自编码的滚动轴承故障分类[J]. 机械工程学报, 2018, 54(7): 87-96.
[15]	李允公, 戴丽, 李国萌, 王波, 吴文寿. 基于听觉显著图的瞬态振动信号表征方法研究[J]. 机械工程学报, 2018, 54(7): 54-62.