基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究

摘要
图/表
参考文献(16)
相关文章 (15)

全文: PDF (2010 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

针对传统滚动轴承故障诊断方法存在抗噪性差、需要人工特征提取、计算量较大、对运行设备要求高的问题，提出一种基于多分支深度可分离卷积神经网络(MBDS-CNN)的滚动轴承故障诊断方法，利用深度可分离卷积和权重剪枝技术对模型尺寸进行压缩，通过多分支结构保证模型的精度，避免梯度消失现象的发生。使用模型尺寸、诊断精度、预测速度作为评价指标对模型进行评估。试验结果证明，基于多分支深度可分离卷积神经网络的滚动轴承故障诊断，可以在噪声环境下有效识别轴承不同部位故障程度，提高了诊断效率，降低了对运行设备性能的要求。

	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
	刘恒畅1
	2
	姚德臣1
	2
	杨建伟1
	2
	张骄3

关键词 ：滚动轴承, 故障程度, 抗噪性, 卷积神经网络(CNN), 故障诊断

Abstract：

Aiming at the disadvantages of traditional rolling bearing fault diagnosis methods, such as poor robust, need for artificial feature extraction, large amount of computation, and high requirements for the running equipment, a fault diagnosis method for rolling bearings based on a multi branch depth seperable convolutional neural network (MBDS-CNN) was proposed.Using the depth separable convolution and weight pruning technology to compress the model size, the multi-branch structure ensures the accuracy of the model and avoids the phenomenon of gradient disappearance.The model was evaluated by using a test set, using the model size, diagnostic accuracy, and prediction speed as evaluation indicators.The experimental results show that the fault diagnosis method for rolling bearings based on the MBDS-CNN can effectively identify the fault degree of different parts of the bearing in the noise environment, improve the diagnostic efficiency and reduce the performance requirements of the running equipment.

Key words： rolling bearing fault degree anti-noise convolutional neural network(CNN) fault diagnosis

收稿日期: 2020-02-25 出版日期: 2021-05-28

引用本文:

刘恒畅1,2，姚德臣1,2，杨建伟1,2，张骄3. 基于多分支深度可分离卷积神经网络的滚动轴承故障诊断研究[J]. 振动与冲击, 2021, 40(10): 95-102.
LIU Hengchang1,2，YAO Dechen1,2，YANG Jianwei1,2，ZHANG Jiao3. Fault diagnosis of rolling bearings based on a multi branch depth separable convolutional neural network. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(10): 95-102.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I10/95

［1］MA S J, CHENG B, SHANG Z W, et al.Scattering transform and LSPTSVM and based fault diagnosis of rotating machinery［J］.Mechanical Systems and Signal Processing, 2018,104: 155-170.

［2］姚德臣,杨建伟,程晓卿,等.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.
YAO Dechen, YANG Jianwei, CHENG Xiaoqing, et al.Railway rolling bearing fault diagnosis based on muti-scale IMF permutation entropy and SA-SVM classifier［J］.Journal of Mechanical Engineering, 2018,54(9): 169-176.

［3］雷亚国,杨彬,杜兆钧,等.大数据下机械装备故障的深度迁移诊断方法［J］.机械工程学报, 2019,55(7): 1-8.
LEI Yaguo, YANG Bin, DU Zhaojun, et al.Deep transfer diagnosis method for machinery in big data era［J］.Journal of Mechanical Engineering, 2019,55(7): 1-8.

［4］LIU H C, YAO D C, YANG J W, et al.Lightweight convolutional neural network and its application in rolling bearing fault diagnosis under variable working conditions［J］.Sensors, 2019,19(22): 4827.

［5］LIU R N, YANG B Y, ZIO E, et al.Artificial intelligence for fault diagnosis of rotating machinery: a review［J］.Mechanical Systems and Signal Processing, 2018,108: 33-47.

［6］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.

［7］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 Xianrong, et al.Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network［J］.Journal of Vibration and Shock, 2018,37(19): 124-131.

［8］MA S J, CAI W, LIU W K, et al.A lighted deep convolutional neural network based fault diagnosis of rotating machinery［J］.Sensors, 2019,19(10): 2381.

［9］NGUYEN D, KANG M, KIM C H, et al.Highly reliable state monitoring system for induction motors using dominant features in a two-dimension vibration signal［J］.New Review of Hypermedia and Multimedia, 2013,19(3/4): 248-258.

［10］ZEILER M D, FERGUS R.Visualizing and understanding convolutional networks［C］//Proceedings of the European Conference on Computer Vision.Berlin: Springer, 2014.

［11］CHOLLET F.Xception: deep learning with depthwise s-eparable convolutions［C］//IEEE Conference on Computer Vision and Pattern Recognition.Honolulu: IEEE, 2017.

［12］HOWARD A G, ZHU M L, CHEN B, et al.Mobilenets: efficient convolutional neural networks for mobile vision applications［C］//IEEE Conference on Computer Vision and Pattern Recognition.Honolulu: IEEE, 2017.

［13］HAN S, MAO H, DALLY W J.Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding［C］//International Conference on Learning Representations.San Juan: PUR, 2016.

［14］SMITH W A, RANDALL R B.Rolling element bearing diagnostics using the Case Western Reserve University data: a bench mark study［J］.Mechanical Systems and Signal Processing, 2015,64/65: 100-131.

［15］PHAM D H, LE A C.Exploiting multiple word embeddings and one-hot character vectors for aspect-based sentiment analysis［J］.International Journal of Approximate Reasoning, 2018,103: 1-10.

［16］MAATEN L, HINTON G.Visualizing data using t-SNE［J］.Journal of Machine Learning Research, 2008(9): 2579-2605.