基于对不平衡数据集进行二次迁移学习的滚动轴承剥落类故障诊断方法

doi:10.16183/j.cnki.jsjtu.2022.008

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (11): 1512-1521.doi: 10.16183/j.cnki.jsjtu.2022.008

所属专题：《上海交通大学学报》2023年“机械与动力工程”专题

基于对不平衡数据集进行二次迁移学习的滚动轴承剥落类故障诊断方法

郭俊锋, 王淼生(), 王智明

兰州理工大学机电工程学院, 兰州 730050

收稿日期:2022-01-20 修回日期:2022-03-02 接受日期:2022-03-14 出版日期:2023-11-28 发布日期:2023-12-01
通讯作者: 王淼生,硕士生;E-mail:wmiaos@163.com.
作者简介:郭俊锋(1978-),教授,主要从事现代测试与故障诊断技术研究.
基金资助:
国家自然科学基金资助项目(51465034)

Fault Diagnosis of Rolling Bearing with Roller Spalling Based on Two-Step Transfer Learning on Unbalanced Dataset

GUO Junfeng, WANG Miaosheng(), WANG Zhiming

School of Mechanical and Electronic Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Received:2022-01-20 Revised:2022-03-02 Accepted:2022-03-14 Online:2023-11-28 Published:2023-12-01

摘要/Abstract

摘要：

滚动轴承在运行过程中正常工作状态时间长,故障时间很短,导致数据集不平衡,从而极大地影响深度学习模型故障诊断的准确率.针对该问题,提出一种基于二次迁移学习的滚动轴承不平衡数据集故障诊断方法.首先使用源域和目标域中的少量数据通过条件梯度惩罚生成对抗网络(CWGAN-GP)生成过渡数据集,然后将搭建好的卷积神经网络模型在源域数据集、过渡数据集和目标域数据集之间进行两次迁移,最后使用目标域的少量数据对迁移后的模型进行微调,得到最终的故障诊断模型.实验结果表明,该方法对不同工况下数据集不平衡的滚动轴承剥落类故障有较好的诊断识别效果.

关键词: 迁移学习, 故障诊断, 不平衡数据集, 生成对抗网络

Abstract:

Under operating conditions, bearings have a substantial service life with short failure time periods, which leads to unbalanced dataset and greatly affects the accuracy of deep learning model fault diagnosis. To address this problem, a fault diagnosis method of rolling bearing unbalanced dataset based on two-step transfer learning is proposed in this paper. First, a small amount of data in the source and target domains is used to generate the transition dataset by conditional gradient penalized generative adversarial network (CWGAN-GP). Then, the constructed convolutional neural network model is migrated twice between the source domain dataset, the transition dataset, and the target domain dataset. Finally, a small amount of data from the target domain is used to fine-tune the transferred model to obtain the final fault diagnosis model. The experimental results show that the method has a good diagnostic recognition effect on rolling bearing spalling class faults with unbalanced dataset under different working conditions.

Key words: transfer learning, fault diagnosis, unbalanced dataset, generative adversarial network

中图分类号:

TH17

郭俊锋, 王淼生, 王智明. 基于对不平衡数据集进行二次迁移学习的滚动轴承剥落类故障诊断方法[J]. 上海交通大学学报, 2023, 57(11): 1512-1521.

GUO Junfeng, WANG Miaosheng, WANG Zhiming. Fault Diagnosis of Rolling Bearing with Roller Spalling Based on Two-Step Transfer Learning on Unbalanced Dataset[J]. Journal of Shanghai Jiao Tong University, 2023, 57(11): 1512-1521.

图/表 12

图1

图2

图3

图4

图5

图6

图7

表1

图8

图9

图10

图11

参考文献 20

[1]	张根保, 李浩, 冉琰, 等. 一种用于轴承故障诊断的迁移学习模型[J]. 吉林大学学报(工学版), 2020, 50(5): 1617-1626.
	ZHANG Genbao, LI Hao, RAN Yan, et al. A transfer learning model for bearing fault diagnosis[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(5): 1617-1626.
[2]	周生通, 朱经纬, 周新建, 等. 组合载荷作用下动车牵引电机转子系统弯扭耦合振动特性[J]. 交通运输工程学报, 2020, 20(1): 159-170.
	ZHOU Shengtong, ZHU Jingwei, ZHOU Xinjian, et al. Bending-torsional coupling vibration characteristics of EMU traction motor rotor system under combined loads[J]. Journal of Traffic and Transportation Engineering, 2020, 20(1): 159-170.
[3]	韩毅. 地铁车辆滚动轴承振动信号的时域分析[J]. 城市轨道交通研究, 2021, 24 (Sup.1): 57-62.
	HAN Yi. Time domain analysis of vibration signal for metro vehicle rolling bearing[J]. Urban Mass Transit, 2021, 24 (Sup.1): 57-62.
[4]	李舜酩, 侯钰哲, 李香莲. 滚动轴承振动故障时频域分析方法综述[J]. 重庆理工大学学报(自然科学), 2021, 35(10): 85-93.
	LI Shunming, HOU Yuzhe, LI Xianglian. Review on time-frequency-domain analysis methods for vibration faults of rolling bearings[J]. Journal of Chongqing University of Technology (Natural Science), 2021, 35(10): 85-93.
[5]	张士强. 基于深度学习的故障诊断技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	ZHANG Shiqiang. Research on fault diagnosis technology based on deep learning[D]. Harbin:Harbin Institute of Technology, 2018.
[6]	ZHAO R, YAN R Q, CHEN Z H, et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213-237. doi: 10.1016/j.ymssp.2018.05.050
[7]	TAMILSELVAN P, WANG P F. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2013, 115: 124-135. doi: 10.1016/j.ress.2013.02.022 URL
[8]	HOANG D T, KANG H J. Rolling element bearing fault diagnosis using convolutional neural network and vibration image[J]. Cognitive Systems Research, 2019, 53: 42-50. doi: 10.1016/j.cogsys.2018.03.002 URL
[9]	ZHAO X L, JIA M P, LIU Z. Fault diagnosis framework of rolling bearing using adaptive sparse contrative auto-encoder with optimized unsupervised extreme learning machine[J]. IEEE Access, 2019, 8: 99154-99170. doi: 10.1109/Access.6287639 URL
[10]	ZHANG X, HUANG T, WU B, et al. Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples[J]. Frontiers of Mechanical Engineering, 2021, 16(2): 340-352. doi: 10.1007/s11465-021-0629-3
[11]	SHAO S Y, WANG P, YAN R Q. Generative adversarial networks for data augmentation in machine fault diagnosis[J]. Computers in Industry, 2019, 106: 85-93. doi: 10.1016/j.compind.2019.01.001 URL
[12]	WANG J, HE Q B. Wavelet packet envelope manifold for fault diagnosis of rolling element bearings[J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(11): 2515-2526. doi: 10.1109/TIM.2016.2566838 URL
[13]	SOUALHI A, MEDJAHER K, ZERHOUNI N. Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(1): 52-62. doi: 10.1109/TIM.2014.2330494 URL
[14]	CHEN J L, LI Z P, PAN J, et al. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 2016, 70/71: 1-35. doi: 10.1016/j.ymssp.2015.08.023 URL
[15]	GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139-144. doi: 10.1145/3422622 URL
[16]	ZHENG M, LI T, ZHU R, et al. Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification[J]. Information Sciences, 2020, 512: 1009-1023. doi: 10.1016/j.ins.2019.10.014 URL
[17]	ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein gan[EB/OL]. (2017-12-06)[2022-01-20]. https://arxiv.org/abs/1701.07875
[18]	ADDAGARLA S K. Real time multi-scale facial mask detection and classification using deep transfer learning techniques[J]. International Journal of Advanced Trends in Computer Science and Engineering, 2020, 9(4): 4402-4408. doi: 10.30534/ijatcse/2020/33942020 URL
[19]	PENG C, LI L L, CHEN Q, et al. A fault diagnosis method for rolling bearings based on parameter transfer learning under imbalance data sets[J]. Energies, 2021, 14(4): 944. doi: 10.3390/en14040944 URL
[20]	TAN B, ZHANG Y, PAN S J, et al. Distant domain transfer learning[C]// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California, USA: ACM, 2017: 2604-2610.

序号	故障类型	源域 0 HP	目标域
序号	故障类型	源域 0 HP	0 HP	1 HP	2 HP
1	Normal	400	400	400	400
2	I(1)	400	400	400	400
3	I(2)	400	400	400	400
4	I(3)	400	400	400	400
5	B(1)	400	400	400	400
6	B(2)	400	400	400	400
7	B(3)	400	400	400	400
8	O(1)	400	400	400	400
9	O(2)	400	400	400	400
10	O(3)	400	400	400	400

基于对不平衡数据集进行二次迁移学习的滚动轴承剥落类故障诊断方法

Fault Diagnosis of Rolling Bearing with Roller Spalling Based on Two-Step Transfer Learning on Unbalanced Dataset

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李明爱1,2,3，许冬芹1. 综述：运动想像脑机接口中的迁移学习[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(1): 37-59.
[2]	钟智伟, 王誉翔, 黄亦翔, 肖登宇, 夏鹏程, 刘成良. 基于概率稀疏自注意力的IGBT模块剩余寿命跨工况预测[J]. 上海交通大学学报, 2023, 57(8): 1005-1015.
[3]	刘家赫, 胡彭炜, 程海龙. 基于性能退化的复杂系统视情维修技术研究[J]. 空天防御, 2023, 6(1): 56-62.
[4]	许勇, 蔡云泽, 宋林. 基于数据驱动的核电设备状态评估研究综述[J]. 上海交通大学学报, 2022, 56(3): 267-278.
[5]	刘秀丽, 徐小力. 基于特征金字塔卷积循环神经网络的故障诊断方法[J]. 上海交通大学学报, 2022, 56(2): 182-190.
[6]	李钰, 杨道勇, 刘玲亚, 王易因. 利用生成对抗网络实现水下图像增强[J]. 上海交通大学学报, 2022, 56(2): 134-142.
[7]	唐泽宇, 邹小虎, 李鹏飞, 张伟, 余佳奇, 赵耀东. 基于迁移学习的小样本OFDM目标增强识别方法[J]. 上海交通大学学报, 2022, 56(12): 1666-1674.
[8]	马航宇, 周笛, 卫宇杰, 吴伟, 潘尔顺. 变工况下基于自适应深度置信网络的轴承智能故障诊断[J]. 上海交通大学学报, 2022, 56(10): 1368-1377.
[9]	沈慧, 刘世民, 许敏俊, 黄德林, 鲍劲松, 郑小虎. 面向加工领域的数字孪生模型自适应迁移方法[J]. 上海交通大学学报, 2022, 56(1): 70-80.
[10]	何新林, 戚宗锋, 李建勋. 基于隐变量后验生成对抗网络的不平衡学习[J]. 上海交通大学学报, 2021, 55(5): 557-565.
[11]	王悦行, 吴永国, 徐传刚. 基于深度迁移学习的红外舰船目标检测算法[J]. 空天防御, 2021, 4(4): 61-66.
[12]	祁生勇, 臧月进, 吕国云, 杜明. 基于生成对抗网络的空中目标图像生成算法研究[J]. 空天防御, 2021, 4(2): 67-.
[13]	姜宇迪, 胡晖, 殷跃红. 基于无监督迁移学习的电梯制动器剩余寿命预测[J]. 上海交通大学学报, 2021, 55(11): 1408-1416.
[14]	胡晓强，仲训昱，张霄力，彭侠夫，何荧. 基于支持向量机辅助的四轴陀螺两级故障诊断方法[J]. 上海交通大学学报, 2020, 54(11): 1151-1156.
[15]	逯程1，徐廷学1，王虹2. 基于属性粒化聚类与回声状态网络的末制导雷达故障诊断[J]. 上海交通大学学报（自然版）, 2018, 52(9): 1112-1119.