自适应卷积神经网络在旋转机械故障诊断中的应用

摘要
图/表
参考文献(29)
相关文章 (15)

全文: PDF (2264 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

针对基于机器学习模型的故障诊断存在依赖人工特征提取质量、维数灾难问题和卷积神经网络（CNN）模型构建缺乏自适应性等问题，提出了一种基于粒子群优化（PSO）算法的自适应CNN故障诊断方法，并将其应用于旋转机械故障诊断。将一维时域信号变成二维时频图像；使用PSO算法对CNN模型中的7个关键参数进行优化选取，以构建深度学习模型；将二维时频图像输入优化后的深度学习模型，对旋转机械故障进行诊断。结果表明，所提方法具有较高的准确率、稳定性和自适应性。

	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
	李涛1
	段礼祥1
	张东宁2
	赵赏鑫2
	黄辉3
	毕彩霞3
	袁壮1

关键词 ：旋转机械')" href="#">

旋转机械, 故障诊断, 卷积神经网络（CNN）, 深度学习模型, 粒子群优化(PSO)算法

Abstract：

Aiming at solving the problems of fault diagnosis based on machine learning model, such as relying on manual feature extraction quality, dimension disaster, lack of selfadaptation of convolutional neural network (CNN) model construction,a fault diagnosis method of adaptive CNN based on particle swarm optimization (PSO) was proposed and applied to fault diagnosis of rotating machinery. Firstly, onedimensional timedomain signal was transformed into twodimensional timefrequency image; Then, the PSO algorithm was employed to optimize the seven key parameters in the CNN model to construct a deep learning model. Finally, the twodimensional timefrequency image was input into the optimized model to diagnose rotating machinery faults. The results show that the proposed method has high accuracy, stability and adaptability.

Key words： rotating machinery')" href="#">

rotating machinery fault diagnosis convolutional neural network(CNN) deep learning model particle swarm optimization(PSO) algorithm

收稿日期: 2019-06-26 出版日期: 2020-08-15

引用本文:

李涛1，段礼祥1，张东宁2，赵赏鑫2，黄辉3,毕彩霞3,袁壮1. 自适应卷积神经网络在旋转机械故障诊断中的应用[J]. 振动与冲击, 2020, 39(16): 275-282.
LI Tao1, DUAN Lixiang1, ZHANG Dongning2, ZHAO Shangxin2, HUANG Hui3, BI Caixia3, YUAN Zhuang1. Application of adaptive convolutional neural network in rotating machinery fault diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(16): 275-282.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I16/275

[1] 张伟．基于卷积神经网络的轴承故障诊断算法研究[D]．哈尔滨：哈尔滨工业大学，2017.
ZHANG Wei．Study on bearing fault diagnosis algorithm based on convolutional neural network[D]．Harbin：Harbin Institute of Technology，2017.
[2] 雷亚国，贾峰，周昕，等．基于深度学习理论的机械装备大数据健康监测方法[J]．机械工程学报，2015，51(21)：49-56.
LEI Y G，JIA F，ZHOU X，et al．A deep learning-based method for machinery health monitoring with big data[J]．Journal of Mechanical Engineering，2015，51(21)：49-56.
[3] 国务院．国务院关于印发《中国制造2025》的通知[EB/OL]．http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm，2015-5-19．
State Council．Notice of the state council on printing and distributing "Made in China 2025"[EB/OL]．http://www.gov.cn/zhengce/content/2015-05/19/content_9784.htm，2015-5-19．
[4] 王新，闫文源．基于变分模态分解和SVM的滚动轴承故障诊断[J]．振动与冲击，2017，36(18)：252-256.
WANG Xin，YAN Wen-yuan．Fault diagnosis of roller element bearings based on variational mode decomposition and SVM[J]．Journal of Vibration and Shock，2014，33(7)：252-256.
[5] 唐贵基，邓飞跃，何玉灵，等．基于时间-小波能量谱熵的滚动轴承故障诊断研究[J]．振动与冲击，2014，33(7)：68-72.
TANG Gui-ji，DENG Fei-yue，HE Yu-lin．Rolling element bearing fault diagnosis based on time-wavelet energy spectrum Entropy[J]．Journal of Vibration and Shock，2014，33(7)：68-72.
[6] 邹龙庆，陈桂娟，邢俊杰，等．基于LMD样本熵与SVM的往复压缩机故障诊断方法[J]．噪声与振动控制，2014，34(6):174-177.
ZOU Long-qing，CHEN Gui-juan，XING Jun-jie，et al．Fault diagnosis method Based on LMD sample entropy and SVM for reciprocating compressors[J]．Noise and Vibration Control，2014，34(6)：174-177.
[7] 金棋，王友仁，王俊．基于深度学习多样性特征提取与信息融合的行星齿轮箱故障诊断方法[J]．中国机械工程，2019，30(2)：196-204.
JIN Qi，WANG You-ren，WANG Jun．Planetary gearbox fault diagnosis based on multiple feature extraction and information fusion combined with deep learning[J]．China Mechanical Engineering，2019，30(2)：196-204.
[8] 张晴晴，刘勇，潘接林，等．基于卷积神经网络的连续语音识别[J]．工程科学学报，2015，37(9)：1212-1217.
ZHANG Q Q，LIU Y，PAN J L，et al．Continuous speech recognition based on convolutional neural network[J]．Chinese Journal of Engineering，2015，37(9)：1212-1217.
[9] KＲIZHEVSKY A，SUTSKEVEＲ I，HINTON G E．ImageNet Classification with Deep Convolutional Neural Networks[C] // International Conference on Neural Information Processing Systems．Lake Tahoe: Curran Associates Inc，2012．
[10] SZEGEDY C，LIU W，JIA Y，et al．Going deeper with
convolutions[C] // IEEE Conference on Computer Vision and Pattern Recognition．Boston：IEEE，2015．
[11] HE K，ZHANG X，REN S，et al．Deep residual learning for image recognition [C] // Computer Vision and Pattern Recognition．Las Vegas：IEEE，2016．
[12] 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).
LI Heng，ZHANG Qin，QIN Xianrong，et al．Bearing fault diagnosis method based on short time Fourier transform and convolutional neural network[J]．Journal of vibration and shock，2018，37(9)．
[13] CHEN Z Q，Li C，SANCHEZ R V．Gearbox fault identification and classification with convolutional neural networks[J]．Shock and Vibration，2015，2015(2)：1-10.
[14] LIU C，CHENG G，CHEN X H，et al．Planetary gears feature extraction and fault diagnosis method based on VMD and CNN[J]．Sensors，2018，18(5)：1523.
[15] DONG H Y，YANG L X，Li H W．Small fault diagnosis of front-end speed controlled wind generator based on deep learning[J]．Wseas Transcations on Circuits and Systems．2016，15:64-72.
[16] ZHANG W, LI C H, PENG G L, et al. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load[J]. Mechanical Systems and Signal Processing, 2018, 100:439-453.
[17] GUO X J，SHEN L，SHEN C Q，et al．Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]．Measurement，2016，93：490-502.
[18] AI J L，YANG X Z．Fault diagnosis of aero-engine based on self-adaptive neural network[J]．Scientia Sinica，2018，48(3)：326-335.
[19] WANG F A，JIANG H k，SHAO H D，et al．An adaptive deep convolutional neural network for rolling bearing fault diagnosis[J]．Measurement Science & Technology，2017，28(9).
[20] TANG S H，SHEN C Q，WANG D，et al．Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis[J]．Neurocomputing，2018，305：1-14.
[21] SHAO H D，JIANG H K，WANG F A，et al．Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet[J]．Isa Transactions，2017，69：187-201.
[22] 周飞燕，金林鹏，董军．卷积神经网络研究综述[J]．计算机学报，2017，40(6)：229-1251.
ZHOU F Y，JIN L P，DONG J．Review of convolutional neural network [J]．Chinese Journal of Computers，2017，40(6)：1229-1251.
[23] JING L Y，WANG T Y，ZHAO M，et al．An adaptive multi-Sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox[J]．Sensors，2017，17(2)：414.
[24] VERSTRAETE D，FERRADA A，DROGUETT E L，et al．Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings[J]．Shock and Vibration，2017，2017：1-17.
[25] SZEGEDY C，VANHOUCKE V，IOFFE S, et al．Rethinking the inception architecture for computer Vision[J]．2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)．2016:2818-2826.
[26] BERGSTRA J，BARDENET R，BENGIO Y，et al．Algorithms for hyper-parameter optimization[C] // International Conference on Neural Information Processing Systems．Curran Associates Inc．2011.
[27] DING X，HE Q，Energy-fluctuated multiscale feature learning with deep convNet for intelligent spindle bearing fault diagnosis[J]. IEEE Transactions on Instrumentation & Measurement，2017，66(8)：1926-1935.
[28] YOU W，SHEN C Q，GUO X J，et al．A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery[J]．Advances in Mechanical Engineering，2017，9(6)：1-17．
[29] MAATEN L V D，Hinton G E．Visualizing data using data[J]．Journal of Machine Learning Research，2008，9(2605)：2579-2605．