基于shapelets时间序列的多源迁移学习滚动轴承故障诊断方法

doi:10.3969/j.issn.1004-132X.2022.24.010

中国机械工程 ›› 2022, Vol. 33 ›› Issue (24): 2990-2996,3006.DOI: 10.3969/j.issn.1004-132X.2022.24.010

基于shapelets时间序列的多源迁移学习滚动轴承故障诊断方法

李可1,2;燕晗1,2;顾杰斐1,2;宿磊1,2;苏文胜3;薛志钢3

1.江南大学江苏省食品先进制造装备技术重点实验室，无锡，214122
2.江南大学机械工程学院，无锡，214122
3.江苏省特种设备安全检验监督研究院无锡分院，无锡，214071

出版日期:2022-12-25 发布日期:2023-01-12
通讯作者: 顾杰斐（通信作者），男，1992年生，副教授。研究方向为故障诊断与智能检测。E-mail:jfgu@jiangnan.edu.cn。
作者简介:李可，男，1978年生，教授、博士研究生导师。研究方向为故障诊断与振动分析。E-mail:like_jiangnan@163.com。
基金资助:
国家自然科学基金（51775243，52175096）；中国博士后科学基金（2021T140279）；江苏省市场监督管理局科技计划（KJ196043）

Research on Multi-source Transfer Learning Bearing Fault Diagnosis Based on Shapelets Time Series

LI Ke1,2 ;YAN Han1,2;GU Jiefei1,2 ;SU Lei1,2; SU Wensheng3;XUE Zhigang3

1.Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology,Jiangnan University,Wuxi,Jiangsu，214122
2.School of Mechanical Engineering,Jiangnan University,Wuxi,Jiangsu，214122
3.Jiangsu Province Special Equipment Safety Supervision Inspection Institute Branch of Wuxi,Wuxi,Jiangsu，214071

Online:2022-12-25 Published:2023-01-12

摘要/Abstract

摘要： 针对滚动轴承故障诊断在工程实际中故障数据稀缺的问题，提出一种基于shapelets时间序列的多源迁移学习滚动轴承故障诊断方法。首先利用典型故障信息丰富、标记样本充足的滚动轴承数据构建多源域数据集，使用不同源域的数据对源域特征提取器与分类器进行预训练；然后利用基于动态时间规整的shapelets学习算法提取源域与目标域的shapelets作为判别结构，通过度量判别结构优化源域数据，对源域网络进行微调以得到诊断模型；最后根据每个源域与目标域的shapelets之间的差异，利用自适应域权重对各分类器的结果进行聚合得出诊断结果。实验结果表明，该方法在小样本与强噪声的情况下具有较高的故障诊断准确率。

关键词: 滚动轴承, 故障诊断, shapelets时间序列, 多源迁移学习

Abstract: Aiming at the problems that the available fault data of rolling bearing fault diagnosis were scarce in industrial productions, a multi-source transfer learning bearing fault diagnosis method was proposed based on shapelets time series. Firstly, source domain sets were constructed by the laboratory data which included abundant typical fault information and sufficient label information, and the source domain feature extractor and classifier were trained using the training data of each source domain. Then, the shapelets learning algorithm based on dynamic time warping (DTW) was used to extract the shapelets of the source domain and the target domain as the discriminant structure, the source domain data was optimized through the measurement discriminant structure, and the source domain network classifier was fine tuned to obtain the diagnostic model. Finally, according to the difference between the shapelets of each source domain and the target domain, the results of each classifier were aggregated by using the adaptive domain weight to obtain the diagnosis results. Experimental results show that the proposed method has good fault diagnosis performance in the case of few shot and high noise.

Key words: rolling bearing, fault diagnosis, shapelets time series, multi-source transfer learning

中图分类号:

李可, 燕晗, 顾杰斐, 宿磊, 苏文胜, 薛志钢. 基于shapelets时间序列的多源迁移学习滚动轴承故障诊断方法[J]. 中国机械工程, 2022, 33(24): 2990-2996,3006.

LI Ke, , YAN Han, GU Jiefei, , SU Lei, SU Wensheng, XUE Zhigang. Research on Multi-source Transfer Learning Bearing Fault Diagnosis Based on Shapelets Time Series[J]. China Mechanical Engineering, 2022, 33(24): 2990-2996,3006.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2022.24.010

http://www.cmemo.org.cn/CN/Y2022/V33/I24/2990

参考文献

［1］刘长良, 武英杰, 甄成刚. 基于变分模态分解和模糊C均值聚类的滚动轴承故障诊断［J］. 中国电机工程学报, 2015, 35(13):3358-3365.
LIU Changliang, WU Yingjie, ZHEN Chenggang. Rolling Bearing Fault Diagnosis Based on Variational Mode Decomposition and Fuzzy C Means Clustering［J］. Proceedings of the CSEE, 2015, 35(13):3358-3365.
［2］罗仁泽, 曹鹏, 代云中, 等. 旋转机械故障诊断理论与实现［J］. 仪表技术与传感器, 2014（3）:107-110.
LUO Renze, CAO Peng, DAI Yunzhong, et al. Rotating Machinery Fault Diagnosis Theory and Implementation［J］. Instrument Technique and Sensor, 2014（3）:107-110.
［3］REZAEIANJOUYBARI B, YI S. Deep Learning for Prognostics and Health Management:State of the Art, Challenges, and Opportunities［J］. Measurement, 2020, 163:107929.
［4］余浩帅, 汤宝平, 张楷, 等. 小样本下混合自注意力原型网络的风电齿轮箱故障诊断方法［J］. 中国机械工程, 2021, 32(20):2475-2481.
YU Haoshuai, TANG Baoping, ZHANG Kai, et al. Fault Diagnosis Method of Wind Power Gearbox Based on Hybrid Self Attention Prototype Network with Small Samples［J］. China Mechanical Engineering, 2021, 32(20):2475-2481.
［5］任浩, 屈剑锋, 柴毅, 等. 深度学习在故障诊断领域中的研究现状与挑战［J］. 控制与决策, 2017, 32(8):1345-1358.
REN Hao, QU Jianfeng, CHAI Yi, et al. Deep Learning for Fault Diagnosis:the State of the Art and Challenge［J］. Control and Decision, 2017, 32(8):1345-1358.
［6］INCE T, KIRANYAZ S, EREN L, et al. Real-time Motor Fault Detectionby 1D Convolutional Neural Networks［J］. IEEE Transactions on Industrial Electronics, 2016, 63(11):7067-7075.
［7］ZHANG W, LI C H, GAO 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.
［8］SHAO H, JIANG H, ZHANG H, et al. Electric Locomotive Bearing Fault Diagnosis Using Novel Convolutional Deep Belief Network［J］. IEEE Transactions on Industrial Electronics, 2018, 65(3):2727-2736.
［9］QI G J, LUO J. Small Data Challengesin Big Data Era:a Survey of Recent Progress on Unsupervised and Semi-Supervised Methods［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 99:1-1．
［10］陈超, 沈飞, 严如强. 改进LSSVM迁移学习方法的轴承故障诊断［J］. 仪器仪表学报, 2017, 38(1):33-40.
CHEN Chao, SHEN Fei, YAN Ruqiang. Improved ISSVM Migration Learning Method for Bearing Fault Diagnosis［J］. Chinese Journal of Scientific Instrument, 2017, 38(1):33-40.
［11］HAN T, LIU C, YANG W, et al. A Novel Adversarial Learning Framework in Deep Convolutional Neural Network for Intelligent Diagnosis of Mechanical Faults［J］. Knowledge-based Systems, 2019, 165:474-487.
［12］PANG S, YANG X. A Cross-domain Stacked Denoising Autoencoders for Rotating Machinery Fault Diagnosis under Different Working Conditions［J］. IEEE Access, 2019, 7:7277-7292.
［13］ZHENG H L, ANG R X, YANG Y T, et al. Intelligent Fault Identification Based on Multisource Domain Generalization towards Actual Diagnosis Scenario［J］. IEEE Transactions on Industrial Electronics, 2020, 67(2):1293-1304.
［14］徐丹雅. 基于多源域自适应的机械故障诊断方法研究［D］. 济南：山东大学, 2021.
XU Danya. Research on Mechanical Fault Diagnosis Method Based on Multi-source Domain Adaptation［D］. Jinan:Shandong University, 2021.
［15］SHI Y W, DENG A D, DING X, et al. Multisource Domain Factorization Network for Cross-domain Fault Diagnosis of Rotating Machinery:an Unsupervised Multisource Domain Adaptation Method［J］. Mechanical Systems and Signal Processing, 2022, 164:108219.
［16］FENG Y, CHEN J L, HE S L, et al. Globally Localized Multisource Domain Adaptation for Cross-domain Fault Diagnosiswith Category Shift［J］. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32:1-15.
［17］SUN Q, LIU Y Y, CHUA T S, et al. Meta-transfer Learningfor Few-shot Learning［C］∥IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, 2019:403-412.
［18］YE L, KEOGH E．Time Series Shapelets:a New Primitive for Data Mining［C］∥Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, 2009:947-956.
［19］GRABOCKA J, SCHILLING N, WISTUBA M, et al. Learning Time-series Shapelets［C］∥Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Hildesheim, 2014:392-401.［20］WANG Z, YAN W, OATES T. Time Series Classification from Scratch with Deep Neural Networks:a Strong Baseline ［C］∥International Joint Conference on Neural Networks. Anchorage, 2017:1578-1585.
［21］PETITJEAN F X, KETTERLIN A, GANARSKI P. A Global Averaging Method for Dynamic Time Warping, with Applications to Clustering［J］. Pattern Recognition, 2010, 44(3):678-693.
［22］GLOROT X, BENGIO Y. Understanding the Difficulty of Training Deep Feedforward Neural Networks［J］. Journal of Machine Learning Research, 2010, 9:249-256.
［23］JASON Y, JEFF C, YOSHUA B, et al. How Transferable Are Featuresin Deep Neural Networks［C］∥Proceedings of the 27th Advances in Neural Information Processing Systems. Cambridge, 2014:3320-3328.
［24］PAN S J, TSANG I W, KWOK J T, et al. Domain Adaptation via Transfer Component Analysis［J］. IEEE Transactions on Neural Networks, 2011, 22(2):199-210.
［25］FINN C, ABBEEL P, LEVINE S. Model-agnostic Meta-learning for Fast Adaptation of Deep Networks［C］∥34th International Conference on Machine Learning. Sydney, 2017:1856-1868.

[1]	罗洁思, 于德介, 史美丽. 基于SVD和线调频小波路径追踪的转速波动齿轮箱故障诊断 [J]. J4, 201016, 21(16): 1947-1951.
[2]	胡超, 刘佐民. 基于材料匹配性的球轴承Heathcote黏-滑模型研究 [J]. J4, 201016, 21(16): 1969-1973.
[3]	肖刚, 顾海瑞, 董锦锦, 王琪冰, 陆佳炜. 仿真数据驱动的长期服役电梯导轨故障迁移诊断方法[J]. 中国机械工程, 2024, 35(01): 125-135.
[4]	于树博, 刘占生, 赵辰. 动力学仿真数据驱动的域自适应智能诊断方法[J]. 中国机械工程, 2023, 34(23): 2832-2841.
[5]	陈剑, 许畅, 徐庭亮, . 基于位错叠加法和改进概率神经网络的离心泵故障诊断方法[J]. 中国机械工程, 2023, 34(23): 2854-2861.
[6]	秦国浩, 张楷, 丁昆, 黄锋飞, 郑庆, 丁国富, . 动态宽卷积残差网络的轴承故障诊断方法[J]. 中国机械工程, 2023, 34(18): 2212-2221.
[7]	钱门贵, 陈涛, 于耀翔, 郭亮, 高宏力, 李威霖, . 一种改进基尼指数加权的轴承健康指标构建方法[J]. 中国机械工程, 2023, 34(15): 1813-1819，1855.
[8]	董绍江, 周存芳, 陈里里, 徐向阳. 基于判别性特征提取和双重域对齐的轴承跨域故障诊断[J]. 中国机械工程, 2023, 34(15): 1856-1863.
[9]	蒙康, 滕伟, 彭迪康, 向玲, 柳亦兵. 运行机理与数据双驱动的风电齿轮箱系统故障预警[J]. 中国机械工程, 2023, 34(12): 1476-1485.
[10]	郑近德, 陈焱, 童靳于, 潘海洋. 精细广义复合多元多尺度反向散布熵及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2023, 34(11): 1315-1325.
[11]	张龙, 胡燕青, 赵丽娟, 张号. 多通道信息融合与深度迁移学习的旋转机械故障诊断[J]. 中国机械工程, 2023, 34(08): 966-975.
[12]	张龙, 张号, 王朝兵, 王晓博, 文培田, 赵丽娟. 显式动力学驱动的轴承变工况故障诊断方法[J]. 中国机械工程, 2023, 34(08): 982-992.
[13]	董绍江, 朱朋, 朱孙科, 刘兰徽, 邢镔, 胡小林. 基于仿真数据驱动和领域自适应的滚动轴承故障诊断方法[J]. 中国机械工程, 2023, 34(06): 694-702.
[14]	赵靖, 杨绍普, 李强, 刘永强, . 一种残差注意力迁移学习方法及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2023, 34(03): 332-343.
[15]	赵艺珂, 王家序, 张新, 吴磊, 刘治汶. 基于增强最小熵解卷积的航空发动机故障诊断[J]. 中国机械工程, 2023, 34(02): 193-200.

基于shapelets时间序列的多源迁移学习滚动轴承故障诊断方法

Research on Multi-source Transfer Learning Bearing Fault Diagnosis Based on Shapelets Time Series

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics