多域信息融合结合改进残差密集网络的轴承故障诊断

摘要
图/表
参考文献(30)
相关文章 (15)

全文: PDF (2071 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对滚动轴承原始时域信号信息单一、深度卷积神经网络提取的特征对信息的传递存在差异等问题，本文提出了一种多域信息融合与改进残差密集网络的轴承故障诊断方法。为了获取故障的多方面信息，先对原始数据进行多域变换，再将融合信息输入经卷积注意力改进的残差密集网络进行深度学习。经注意力机制改进的网络能够实现对提取特征的重要性区分，提高网络的训练速度、改善识别准确率。实验结果及对比分析表明本文算法可以提取较为全面的特征，较传统方法具有更好的识别效果。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	袁彩艳1
	孙洁娣1
	2
	温江涛3
	时培明3
	闫盛楠1
	2

关键词 ：轴承故障诊断, 残差网络, 多变换域处理, 注意力机制

Abstract：Aiming at the problem that the original time signal of rolling bearing is relatively simple and the features extracted by convolution neural network are different during useful information transmission, this paper proposed a bearing fault diagnosis based on multi domain information fusion and improved residual dense network. In order to obtain the multifaceted information of fault signal, the original data is transformed in multiple domains, then the multi-domain information is input into the residual dense network improved by convolution attention mechanism for deep learning, realize discrimination according to the importance of the extracted features, and improve the training speed and efficiency of the neural network. Experimental results and analysis show that the proposed method can extract more comprehensive features and has higher recognition accuracy than traditional methods.

Key words： Bearing fault diagnosis Residual network Multiple transformation domain processing Attention mechanism

收稿日期: 2020-09-25 出版日期: 2022-02-28

引用本文:

袁彩艳1，孙洁娣1,2，温江涛3，时培明3，闫盛楠1,2. 多域信息融合结合改进残差密集网络的轴承故障诊断[J]. 振动与冲击, 2022, 41(4): 200-208.
YUAN Caiyan1,SUN Jiedi1,2，WEN Jiangtao3,SHI Peiming3,YAN Shengnan1,2. Bearing fault diagnosis based on information fusion and improved residual dense networks. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(4): 200-208.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I4/200

[1] Li Y F, Liang X H, Zuo M J. Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis [J].Mechanical Systems and Signal Processing, 2017, 85:146-161.
[2] 吴春志,江鹏程,冯辅周等.基于一维卷积神经网络的齿轮箱故障诊断[J].振动与冲击,2018,37(22):56-61.
WU Chun-zhi, JIANG Peng-cheng, FENG Fu-zhou, et al. Faults diagnosis method for gearboxes based on a 1-D convolutional neural network[J]. Journal of Vibration and Shock, 2018, 37(22):56-61.
[3] 李恒,张氢,秦仙蓉,等.基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J].振动与冲击,2018,37(19):124-131．
LI Heng, ZHANG Qing, QIN Xian-rong, et al. Fault diagnosis method for rolling bearings based on short- time Fourier transform and convolutional neural network[J].Journal of Vibration and Shock, 2018,37(19) :124 －131．
[4] 马伦,康建设,孟妍等.基于Morlet小波变换的滚动轴承早期故障特征提取研究[J].仪器仪表学报,2013,34(4): 920-926.
MA Lun, KANG Jian-she, MENG Yan, et al. Research on feature extraction of rolling bearing incipient fault based on Morlet wavelet transformation [J]. Chinese Journal of Scientific Instrument, 2013, 34(4):920-926.
[5] Sun J D, Yan C H, J，Wen J T. Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning [J]. IEEE Transactions on Instrumentation and Measurement, 2018, 67(1):185-195.
[6] 焦卫东,林树森.整体改进的基于支持向量机的故障诊断方法[J].仪器仪表学报, 2015,36 (8):1861-1870．
JIAO Wei-dong, LIN Shu-sen. Overall-improved fault diagnosis approach based on support vector machine [J].Chinese Journal of Scientific Instrument, 2015, 36 (8):1861-1870．
[7] Wang H, Chen P. Intelligent diagnosis method for rolling element bearing faults using possibility theory and neural network[J].Computers & Industrial Engineering, 2011, 60 (4): 511-518.
[8] 周奇才,刘星辰,赵炯,等.旋转机械一维深度卷积神经网络故障诊断研究[J].振动与冲击,2018,37(23):39-45.
ZHOU Qi-cai, LIU Xing-chen, ZHAO Tong, et al. Fault diagnosis for rotating machinery based on 1D depth convolutional neural network[J]. Journal of Vibration and Shock, 2018, 37(23):39-45.
[9] 温江涛,闫常弘,孙洁娣,乔艳雷.基于压缩采集与深度学轴承故障诊断方法[J].仪器仪表学报, 2018, 39(01):171-179.
WEN Jiang-tao, YAN Chang-hong, SUN Ji-di, et al. Bearing fault diagnosis method based on compressed acquisition and deep learning [J]. Chinese Journal of Scientific Instrument, 2018, 39(1):171-179.
[10] 杨平,苏燕辰,张振.基于卷积胶囊网络的滚动轴承故障诊断研究[J]，振动与冲击，2020，39(04):55-62+68.
YANG ping, SU yan-chen, ZHANG zhen, A study on rolling bearing fault diagnosis based on convolution capsule network[J]. Journal of Vibration and Shock, 2020,39(04):55-62+68.
[11] Lu C, Wang Z, Zhou B. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification [J].Advanced Engineering Informatics, 2017, 32: 139-151.
[12] Ince T, Kiranyaz S, Eren L, et al. Real-Time Motor Fault Detection by 1-DConvolutional Neural Networks [J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7067-7075.
[13] Li H M, HUANG J Y, JI S W. Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network[J]. Sensors, 2019, 19(9): 2034.
[14] Liu X,Zhou Q,Zhao J,et al.Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network[J].Sensors,2019,19(4).
[15] CHEN Z Q,LI C, SANCHEZ R.Gearbox fault identification and classification with convolutional neural networks[J].Shock andVibration,2015(2):1-10.
[16] Jiao J, Zhao M, Lin J, et al. Deep Coupled Dense Convolutional Network with Complementary Data for Intelligent Fault Diagnosis [J]. IEEE Transactions on Industrial Electronics, 2019, 66(12): 9858-9867.
[17] He K M, Zhang X Y, Ren S Q,et al. Deep Residual Learning for Image Recognition[C]/ / 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016.
[18] Peng D, Liu Z, Wang H, et al. A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains[J]. IEEE Access, 2018:10278-10293.
[19] 雷亚国,杨彬,杜兆钧,吕娜.大数据下机械装备故障的迁移诊断方法[J].机械工程学报, 2019, 55(07):1-8.
LEI Ya-guo, YANG Bin, DU Zhao-jun, et al. Deep Transfer Diagnosis Method for Machinery in Big Data Era [J].Mechanical Engineering, 2019, 55(7):1-8.
[20] Wei Zhang, Xiang Li, Qian Ding. Deep residual learning-based fault diagnosis method for rotating machinery [J]. ISA Transactions, 2019, 95: 295-305.
[21] Zhao M, Kang M, Tang B, 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.
[22] Ma S, Liu W, Cai W, et al. Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions[J]. IEEE Access,2019,7:57023-57036.
[23] Appana D K,Alexander P,Jong-Myon K.Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks[J].Soft Computing, 2018, 22: 6719-6729.
[24] Sadoughi M,Hu C.Physics-based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings[J]. IEEE Sensors Journal, 2019, 19(11):4181-4192.
[25] Wang J J, Ma Y L, Huang Z G, et al. Performance Analysis and Enhancement of Deep Convolutional Neural Network: Application to Gearbox Condition Monitoring [J]. Business and information systems engineering, 2019, 61(3):311-326.
[26] Mao W T, Ding L,Tian S Y, et al. Online detection for bearing incipient fault based on deep transfer learning [J]. Measurement, 2020, 152: 107278
[27] Kumar M, Vaish A. Encryption of color images using MSVD in DCST domain [J].Optics and Lasers in Engineering, 2017, 88:51-59.
[28] Bini, A.A. Image restoration via DOST and total variation regularization [J]. IET Image Processing, 2019, 13(3): 458-468.
[29] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module [C]. European Conference on Computer Vision, 2018:3-19.
[30] SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study [J]. Mechanical Systems and Signal Processing, 2015, 64-65(12):100-131.