利用改进卷积神经网络的滚动轴承变工况故障诊断方法

赵小强; 张亚洲

doi:10.7652/xjtuxb202112013

您当前的位置：

首页 >

文章列表页 >

利用改进卷积神经网络的滚动轴承变工况故障诊断方法

更新时间：2025-07-09

- 利用改进卷积神经网络的滚动轴承变工况故障诊断方法
- Improved CNN-Based Fault Diagnosis Method for Rolling Bearings under Variable Working Conditions
- 西安交通大学学报 2021年55卷第12期页码：108-118
- 作者机构：
  
  1. 兰州理工大学电气工程与信息工程学院,兰州,730050
  2. 兰州理工大学甘肃省工业过程先进控制重点实验室,兰州,730050
  3. 兰州理工大学国家级电气与控制工程实验教学中心,兰州,730050
- 作者简介：
- 基金信息：
  
  : 2021-05-07。作者简介: 赵小强(1969—),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61763029);甘肃省高等学校产业支撑引导项目(2019C-05);甘肃省工业过程先进控制重点实验室开放基金项目(2019KFJJ01)
- DOI：10.7652/xjtuxb202112013
  中图分类号： TP206.3;TH133.3
- 网络首发：2021-12-10，
  
  纸质出版：2021
- 稿件说明：
移动端阅览
赵小强, 张亚洲. 利用改进卷积神经网络的滚动轴承变工况故障诊断方法[J]. 西安交通大学学报, 2021,55(12):108-118.

Improved CNN-Based Fault Diagnosis Method for Rolling Bearings under Variable Working Conditions[J]. 2021, 55(12): 108-118.
赵小强, 张亚洲. 利用改进卷积神经网络的滚动轴承变工况故障诊断方法[J]. 西安交通大学学报, 2021,55(12):108-118. DOI： 10.7652/xjtuxb202112013.

Improved CNN-Based Fault Diagnosis Method for Rolling Bearings under Variable Working Conditions[J]. 2021, 55(12): 108-118. DOI： 10.7652/xjtuxb202112013.

摘要

针对滚动轴承在强噪声环境和变工况下故障诊断效果不佳、泛化能力差的问题

提出一种改进卷积神经网络(CNN)的滚动轴承变工况故障诊断方法。设计了多尺度特征提取模块

采用不同尺度的卷积层提取对输入数据特征

实现了提取故障数据中特征信息最大化。同时

引入通道注意力机制

提取出该模块中更重要、更关键的信息; 设计了带跳跃连接线的卷积模块

防止提取到的丰富特征在卷积层前向传递时丢失; 以Softmax交叉熵作为损失函数

利用Adam优化算法实现滚动轴承故障诊断。将所提方法分别在凯斯西储大学轴承数据集和变速箱数据集上进行实验验证

结果表明:在凯斯西储大学轴承数据集上的变噪声实验中

所提方法诊断平均准确率为96.49%

在变工况中诊断准确率在90%以上

均高于比较方法; 在变速箱轴承数据集上

所提方法诊断准确率为99.54%

具有较好的抗噪性和更好的泛化能力。

Abstract

Aiming at the worse fault diagnosis of rolling bearing and poor generalization ability in a strong noise environment and variable working conditions

an improved CNN-based fault diagnosis method for rolling bearing under variable working conditions is proposed. A multi-scale feature extraction module is designed

and convolutional layers of different scales are adopted to extract features from the input data to maximize the extraction of feature information in the fault data. The channel attention mechanism is then introduced to extract the more important and critical components from this module. A convolution module with skip connection lines is designed to prevent the extracted rich features from being lost when the convolutional layer is forwarded. Regarding softmax cross entropy as the loss function

the Adam optimization algorithm is chosen to realize the fault diagnosis for rolling bearing. The proposed method is verified by experiments on the bearing dataset and gearbox dataset from Case Western Reserve University. The results show that in the variable noise experiment on the bearing dataset from Case Western Reserve University

the proposed method achieves an average diagnostic accuracy rate of 96.49%

and the diagnostic accuracy rate is beyond 90% in variable working conditions

which are obviously higher than the competing methods. On the gearbox bearing data set

the diagnostic accuracy rate of the proposed method with better noise resistance and generalization ability reaches 99.54%.

关键词

Keywords

references

WANG Huaqing, KE Yanliang, LUO Ganggang, et al. Compressed sensing of roller bearing fault based on multiple down-sampling strategy [J]. Measurement Science and Technology, 2016, 27(2): 025009.

JIANG Hongkai, XIA Yong, WANG Xiaodong. Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a 11/2 dimension spectrum [J]. Measurement Science and Technology, 2013, 24(12): 125002.

张妮, 车立志, 吴小进. 基于数据驱动的故障诊断技术研究现状及展望 [J]. 计算机科学, 2017, 44(Z1): 37-42.

ZHANG Ni, CHE Lizhi, WU Xiaojin. Research status and prospects of data-driven fault diagnosis technology [J]. Computer Science, 2017, 44(S1): 37-42.

张西宁, 郭清林, 刘书语. 深度学习技术及其故障诊断应用分析与展望 [J]. 西安交通大学学报, 2020, 54(12): 1-13.

ZHANG Xining, GUO Qinglin, LIU Shuyu. Analysis and prospect of deep learning technology and its application in fault diagnosis [J]. Journal of Xi'an Jiaotong University, 2020, 54(12): 1-13.

JEGADEESHWARAN R, SUGUMARAN V. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines [J]. Mechanical Systems and Signal Processing, 2015, 52/53: 436-446.

GU Yingkui, ZHOU Xiaoqing, YU Dongping, et al. Fault diagnosis method of rolling bearing using principal component analysis and support vector machine [J]. Journal of Mechanical Science and Technology, 2018, 32(11): 5079-5088.

文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述 [J]. 自动化学报, 2016, 42(9): 1285-1299.

WEN Chenglin, LÜ Feiya, BAO Zhejing, et al. Review of small fault diagnosis methods based on data-driven [J]. Acta Automatica Sinica, 2016, 42(9): 1285-1299.

HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507.

SUN Wenjun, SHAO Siyu, ZHAO Rui, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification [J]. Measurement, 2016, 89: 171-178.

LI Chuan, SANCHEZ R V, ZURITA G, et al. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis [J]. Neurocomputing, 2015, 168: 119-127.

JIANG Guoqian, HE Haibo, YAN Jun, et al. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox [J]. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3196-3207.

JIA Feng, LEI Yaguo, LU Na, et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization [J]. Mechanical Systems and Signal Processing, 2018, 110: 349-367.

WANG Shuhui, XIANG Jiawei. A minimum entropy deconvolution-enhanced convolutional neural networks for fault diagnosis of axial piston pumps [J]. Soft Computing, 2020, 24(4): 2983-2997.

宫文峰, 陈辉, 张泽辉, 等. 基于改进卷积神经网络的滚动轴承智能故障诊断研究 [J]. 振动工程学报, 2020, 33(2): 400-413.

GONG Wenfeng, CHEN Hui, ZHANG Zehui, et al. Intelligent fault diagnosis of rolling bearing based on improved convolutional neural network [J]. Journal of Vibration Engineering, 2020, 33(2): 400-413.

ZHANG Wei, PENG Gaoliang, LI Chuanhao, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals [J]. Sensors, 2017, 17(2): 425.

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 Yaguo, JIA Feng, ZHOU Xin, et al. Mechanical equipment health monitoring method based on deep learning theory [J]. Journal of Mechanical Engineering, 2015, 51(21): 49-56.

LEI Jinhao, LIU Chao, JIANG Dongxiang. Fault diagnosis of wind turbine based on long short-term memory networks [J]. Renewable Energy, 2019, 133: 422-432.

宫文峰, 陈辉, 张美玲, 等. 基于深度学习的电机轴承微小故障智能诊断方法 [J]. 仪器仪表学报, 2020, 41(1): 195-205.

GONG Wenfeng, CHEN Hui, ZHANG Meiling, et al. Intelligent diagnosis method for incipient fault of motor bearing based on deep learning [J]. Chinese Journal of Scientific Instrument, 2020, 41(1): 195-205.

SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4: inception-ResNet and the impact of residual connections on learning [C]∥Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2017: 4278-4284.

BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate [EB/OL]. [2021-04-01]. https: ∥arxiv.org/abs/1409.0473.

袁壮, 董瑞, 张来斌, 等. 深度领域自适应及其在跨工况故障诊断中的应用 [J]. 振动与冲击, 2020, 39(12): 281-288.

YUAN Zhuang, DONG Rui, ZHANG Laibin, et al. Deep domain adaptation and its application in fault diagnosis under different working conditions [J]. Vibration and shock, 2020, 39(12): 281-288.

SHAO Siyu, MCALEER S, YAN Ruqiang, et al. Highly accurate machine fault diagnosis using deep transfer learning [J]. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2446-2455.

赵小强, 梁浩鹏. 使用改进残差神经网络的滚动轴承变工况故障诊断方法 [J]. 西安交通大学学报, 2020, 54(9): 23-31.

ZHAO Xiaoqiang, LIANG Haopeng. Fault diagnosis method for rolling bearing under variable working conditions using improved residual neural network [J]. Journal of Xi'an Jiaotong University, 2020, 54(9): 23-31.

朱浩, 宁芊, 雷印杰, 等. 基于注意力机制-Inception-CNN模型的滚动轴承故障分类 [J]. 振动与冲击, 2020, 39(19): 84-93.

ZHU Hao, NING Qian, LEI Yinjie, et al. Fault classification of rolling bearings based on attention mechanism-inception-CNN model [J]. Journal of Vibration and Shock, 2020, 39(19): 84-93.

张西宁,余迪,刘书语.基于迁移学习的小样本轴承故障诊断方法研究.2021,55(10):30-37.doi:10.7652/xjtuxb202110 004.

张西宁,李霖,刘书语,雷建庚.基于能量峰定位的经验小波变换及在轴承微弱故障诊断中的应用.2021,55(8):1-8.doi:10.7652/xjtuxb202108001.

陈保家,陈学力,沈保明,陈法法,李公法,肖文荣,肖能齐.CNN-LSTM深度神经网络在滚动轴承故障诊断中的应用.2021,55(6):28-36.doi:10.7652/xjtuxb202106004.

张西宁,刘书语,余迪,雷建庚,李霖.改进深度卷积神经网络及其在变工况滚动轴承故障诊断中的应用.2021,55(6):1-8.doi:10.7652/xjtuxb202106001.

吴春志,吴守军,冯辅周,朱俊臻.一种具有强抗噪性的深度学习故障诊断模型.2021,55(4):61-68.doi:10.7652/xjtuxb202104007.

张西宁,郭清林,刘书语.深度学习技术及其故障诊断应用分析与展望.西安交通大学学报,2020,54(12):1-13.doi:10.7652/xjtuxb202012001.

张西宁,张雯雯,周融通,向宙.采用单类随机森林的异常检测方法及应用.2020,54(2):1-8,157.doi:10.7652/xjtuxb 202002001.

张西宁,周融通,郭清林,张雯雯.局部倒频谱编辑方法及其在齿轮箱微弱轴承故障特征提取中的应用.2019,53(12):1-9.doi:10.7652/xjtuxb201912001.

张志强,孙若斌,徐冠基,杨志勃,陈雪峰.采用非相关字典学习的滚动轴承故障诊断方法.2019,53(6):29-34.doi:10.7652/xjtuxb201906005.

张西宁,向宙,夏心锐,李立帆.堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用.2018,52(10):49-56,87.doi:10.7652/xjtuxb201810007.

邓飞跃,强亚文,杨绍普,郝如江,刘永强.一种自适应频率窗经验小波变换的滚动轴承故障诊断方法.2018,52(8):22-29.doi:10.7652/xjtuxb201808004.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

使用改进残差神经网络的滚动轴承变工况故障诊断方法

蜉蝣优化双通道网络在齿轮箱故障诊断中的应用

利用非线性流形学习的轴承早期故障特征提取方法

滚动轴承故障诊断的多小波谱峭度方法

滚动轴承故障诊断的案例推理方法