基于DCAE-CNN的自动倾斜器滚动轴承故障诊断

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摘要针对直升机自动倾斜器滚动轴承工况复杂、噪声干扰大，造成故障诊断效果不佳的问题，提出一种基于深度卷积自编码器(Deep Convolutional AutoEncoder，DCAE)和卷积神经网络(Convolutional Neural Network，CNN)的轴承故障诊断方法。该方法首先采用小波变换方法构造不同状态下振动信号的时频图，然后使用DCAE对时频图进行图像去噪，最后利用CNN对去噪后的时频图进行故障分类。利用课题组和美国凯斯西储大学的滚动轴承故障数据开展诊断实验，并与CNN、堆叠降噪自编码器(Stacked Denoise AutoEncoder，SDAE)两种深度学习方法进行对比，结果表明，本文方法在高噪声环境下具有更高的故障识别率。

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	万齐杨1
	熊邦书1
	李新民2
	孙伟2

关键词 ：故障诊断, 小波时频图, 深度学习, 自动倾斜器

Abstract：In order to solve the problem of large noise interference and poor fault diagnosis of the helicopter swashplate rolling bearing, we proposed a fault diagnosis method based on deep convolutional autoencoder (DCAE) and convolutional neural network (CNN). Firstly, the wavelet transform method is used to construct the time-frequency diagram of the vibration signals in different states. Then, the image denoising is performed on the time-frequency map using DCAE. Finally, the time-frequency map after denoising is classified by CNN. Diagnostic experiments were carried out using the bearing fault data of the research team and Case Western Reserve University, and the proposed method is compared with the CNN and the stacked denoise autoencoder (SDAE). The results show that the proposed method has higher fault recognition rate in high noise environment.

Key words： fault diagnosis wavelet time-frequency diagram deep learning swashplate

收稿日期: 2019-01-10 出版日期: 2020-05-28

引用本文:

万齐杨1，熊邦书1，李新民2，孙伟2. 基于DCAE-CNN的自动倾斜器滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(11): 273-279.
WAN Qiyang1, XIONG Bangshu1, LI Xinmin2， SUN Wei2. Fault diagnosis for rolling bearing of swashplate based on DCAE-CNN. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(11): 273-279.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I11/273

[1] 向丹, 岑健. 基于EMD熵特征融合的滚动轴承故障诊断方法[J]. 航空动力学报, 2015, 30(5):1149-1155.
XIANG Dan,CEN Jian.Method of roller bearing fault diagnosis based on feature fusion of EMD entropy[J].Journal of Aerospace Power,2015, 30(5):1149-1155.
[2] 武哲, 杨绍普, 张建超. 基于LMD自适应多尺度形态学和Teager能量算子方法在轴承故障诊断中的应用[J]. 振动与冲击, 2016, 35(3):7-13.
WU Zhe, YANG Shaopu, ZHANG Jianchao.Bearing fault feature extraction method based on LMD adaptive multiscale morphology and energy operator demodulating[J].Journal of Vibration and Shock, 2016, 35(3):7-13.
[3] 王晓龙, 唐贵基, 周福成. 自适应可调品质因子小波变换在轴承早期故障诊断中的应用[J]. 航空动力学报, 2017, 32(10):2467-2475.
WANG Xiaolong, TANG Guiji, ZHOU Fucheng. Application of adaptive tunable Q-factor wavelet transform on incipient fault diagnosis of bearing[J].Journal of Aerospace Power,2017, 32(10):2467-2475.
[4] RODRÍGUEZ R, LLANES S, BERNAL D L, et al. A novel fault diagnosis scheme applying fuzzy clustering algorithms[J]. Applied Soft Computing, 2017,9:605-619.
[5] CAO C P, LI M, LI Y, et al. Intelligent fault diagnosis of hot die forging press based on binary decision diagram and fault tree analysis[J]. Procedia Manufacturing, 2018,15:459-466.
[6] WANG Y, YANG H B, YUAN X F, et al. An improved Bayesian network method for fault diagnosis[J]. IFAC-PapersOnLine, 2018,51(21):341-346.
[7] 姜久亮, 刘文艺, 侯玉洁, 等. 基于内积延拓LMD及SVM的轴承故障诊断方法研究[J]. 振动与冲击, 2016, 35(6):104-108.
JIANG Jiuliang, LIU Wenyi, HOU Yujie, et al. Bearing fault diagnosis based on integral waveform extension LMD and SVM[J].Journal of Vibration and Shock,2016, 35(6):104-108.
[8] 贺岩松, 黄毅, 徐中明, 等. 基于小波奇异熵与SOFM神经网络的电机轴承故障识别[J]. 振动与冲击, 2017, 36(10):217-223.
HE Yansong, HUANG Yi, XU Zhongming,et al. Motor bearing fault identification based on the wavelet singular entropy and SOFM neural network[J]. Journal of Vibration and Shock, 2017, 36(10):217-223.
[9] HOANGD T, KANG H J. Rolling element bearing fault diagnosis using convolutional neural network and vibration image[J]. Cognitive Systems Research, 2019, 53:42-50.
[10] LI X, ZHANG W, DING Q, et al. Multi-Layer domain adaptation method for rolling bearing fault diagnosis[J]. Signal Processing, 2019, 157:180-197.
[11] ZHAO D Z, WANG T Y, CHU F L. Deep convolutional neural network based planet bearing fault classification[J]. Computers in industry, 2019, 107:59-66.
[12] GUO X, CHEN L, SHEN C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]. Measurement, 2016, 93:490-502.
[13] FANG Z Q, JIA T, CHEN Q S, et al. Laser stripe image denoising using convolutional autoencoder[J]. Results in Physics, 2018, 11:96-104.
[14] LEE D, CHOI S, KIM H J. Performance evaluation of image denoising developed using convolutional denoising autoencoders in chest radiography[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2018, 884:97-104.
[15] ZHANG W, LI C, PENG G, 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 & Signal Processing, 2017, 100:439-453.
[16] WU H, ZHAO J S. Deep convolutional neural network model based chemical process fault diagnosis[J]. Computers & Chemical Engineering, 2018, 115:185-197.
[17] 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):51-56.
WU Chunzhi, JIANG Pengcheng ,FENG Fuzhou,et al. Faults diagnosis method for gearboxes based on a 1-D convolutional neural network[J]. Journal of Vibration and Shock, 2018, 37(22):51-56.
[18] LECUN Y L, BOTTOU L, BENGIO Y, et al. Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.