针对实际工业生产中不同规格和工况下滚动轴承振动数据分布差异大，多个相似数据集资源利用不充分，导致诊断模型准确率不高的问题，提出一种基于多源域异构模型迁移的滚动轴承故障诊断方法。该方法利用短时傅里叶变换获取滚动轴承振动信号的时频谱图；选择多种不同规格和工况下已知标签数据作为多源域，其他规格和工况下少量已知标签数据作为目标域；使用多个源域数据训练多个ResNet-34深度网络，并提出利用基于进化策略的与模型无关元学习改进异构模型参数迁移策略，使其能够自适应决定迁移到目标域的知识层级及内容；提出将源域知识迁移到VGG-16深度网络得到多个目标域模型后，将其提取的特征首尾相接输入同一个极限学习机中实现特征融合和分类，最终建立滚动轴承故障诊断模型。经实验验证，所提方法可实现不同规格和工况下滚动轴承间的迁移诊断问题，并且具有较高的准确率。
Abstract
For the problems of large distribution differences in rolling bearing vibration data under different specifications and working conditions in actual industrial production, and the resources of multiple similar data sets are not fully utilized, which lead to the low accuracy of diagnosis model, a rolling bearing fault diagnosis method based on heterogeneous model transfer in multi-source domain are proposed. Fourier transform is used to obtain the time-frequency spectra of the rolling bearing vibration signals. A variety of labeled data under different specifications and conditions are used as the multi-source domain, and a small amount of labeled data in other specifications and conditions are used as the target domain. The multiple source domains data are used to train ResNet-34 deep networks, the parameter transfer strategy of heterogeneous model is improved by using evolution strategies model agnostic meta learning, so that it can adaptively determine the level and content of knowledge transferred to the target domain. It is proposed to transfer the source domain knowledge to the VGG-16 deep networks to obtain multiple target domain models, the extracted features are input into the same extreme learning machine head to tail to achieve feature fusion and classification, and finally rolling bearing fault diagnosis model can be established. Experimental results show that the proposed method can achieve the transfer diagnosis between rolling bearings under different specifications and working conditions, and has a higher accuracy.
关键词
多源域 /
异构模型 /
元学习 /
滚动轴承 /
故障诊断
{{custom_keyword}} /
Key words
multi-source domain /
heterogeneous model /
meta learning /
rolling bearing /
fault diagnosis
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 康守强，邹佳悦，王玉静，等. 基于无监督特征对齐的变负载下滚动轴承故障诊断方法[J]. 中国电机工程学报，2020，40(1)：274-281.
KANG Shou-qiang, ZOU Jia-yue, WANG Yu-jing, et al. Fault diagnosis method of a rolling bearing under varying loads based on unsupervised feature alignment[J]. Proceedings of the CSEE, 2020, 40(1): 274-281.
[2] 李益兵，黄定洪，马建波，等. 基于深度置信网络与信息融合的齿轮故障诊断方法[J]. 振动与冲击，2021，40(8)：62-69.
LI Yi-bing, HUANG Ding-hong, MA Jian-bo, et al. A gear fault diagnosis method based on deep belief network and information fusion[J]. Journal of Vibration and Shock, 2021, 40(8): 62-69.
[3] 杨毅，范栋琛，殷浩然，等. 基于深度迁移学习的输电线路故障选相模型及可迁移性研究[J]. 电力自动化设备，2020，40(10)：165-172.
YANG Yi, FAN Dong-chen, YIN Haoran, et al. Transmission line fault phase selection model based on deep-migration learning and its transferability research[J]. Electric Power Automation Equipment, 2020, 40(10): 165-172.
[4] Fu L, Zhang L, Tao J. An improved deep convolutional neural network with multiscale convolution kernels for fault diagnosis of rolling bearing[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1043(5): 052021.
[5] 赵志宏，赵敬娇，魏子洋，等. 基于BiLSTM的滚动轴承故障诊断研究[J]. 振动与冲击，2021，40(1)：95-101.
ZHAO Zhi-hong, ZHAO Jing-jiao, WEI Zi-yang. Rolling bearing fault diagnosis based on BiLSTM network[J]. Journal of Vibration and Shock, 2021, 40(1): 95-101.
[6] Wu Z H, Jiang H K, Zhao K, et al. An adaptive deep transfer learning method for bearing fault diagnosis[J]. Measurement. 2020, (151): 2-29.
[7] Shao S, Yan R Q, Lu Y, et al. DCNN-Based Multi-Signal Induction Motor Fault Diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 2658-2669.
[8] 邵海东，张笑阳，程军圣，等. 基于提升深度迁移自动编码器的轴承智能故障诊断[J]. 机械工程学报，2020，56(9)：84-90.
SHAO Hai-dong, ZHANG Xiao-yang, CHENG Jun-sheng, et al. Intelligent fault diagnosis of bearing using enhanced deep transfer auto-encoder[J]. Journal of mechanical engineering, 2020, 56(9): 84-90.
[9] Jiang F, Shen X, Jiang F, et al. A bearing fault diagnosis method based on improved convolution neural network and transfer learning[C]//International Conference on Intelligent Equipment and Special Robots (ICIESR 2021). SPIE, 2021, 12127: 124-130.
[10] 侯东晓，穆金涛，方成，时培明. 基于GADF与引入迁移学习的ResNet34对变速轴承的故障诊断[J]. 东北大学学报(自然科学版)，2022，43(03)：383-389.
HOU Dong-xiao, MU Jin-tao, FANG Cheng, et al. Fault Diagnosis of Variale Speed Bearings Based on GADF and ResNet34 Introduced Transfer Learning[J]. Journal of Northeastern University(Natural Science), 2022, 43(03): 383-389.
[11]邵海东，肖一鸣，颜深. 仿真数据驱动的改进无监督域适应轴承故障诊断[J/OL]. 机械工程学报: 1-10[2022-09-16]. http://kns.cnki.net/kcms/detail/11.2187.TH.20220915.1441.002.html
Shao Hai-dong, Xiao Yi-ming, Yan Shen. Simulation Data-driven Enhanced Unsupervised Domain Adaptation for Bearing Fault Diagnosis[J/OL]. Journal of Mechanical Engineering: 1-10[2022-09-16]. http://kns.cnki.net/kcms/
detail/11.2187.TH.20220915.1441.002.html
[12] CHUNFENG W, ZHENG L, JUN Z, et al. Heterogeneous Transfer Learning Based on Stack Sparse Auto-Encoders for Fault Diagnosis[C]//Chinese Automation Congress, 2018, 4277-4281.
[13] Yunhun J , Hankook Le, Sung J. Learning What and Where to Transfer[EB/OL]. http://arxiv.org/abs/1905.0590, 2019.
[14] 杨胜康，孔宪光，程涵，等. 基于多源域深度迁移学习的机械故障诊断[J]. 振动与冲击，2022，41(9)：32-40.
YANG Shengkang, SUN Xuanguang, CHEN Han, et al. Mechanical fault diagnosis based on multi-source domain deep transfer learning[J]. Journal of Vibration and Shock, 2022, 41(9): 32-40.
[15] Bin Y, Songci X, Yagou L, et al. Multi-source transfer learning network to complement knowledge for intelligent diagnosis of machines with unseen faults[EB/OL]. http://doi.org/10.1016/j.ymssp, 2021.
[16] Xing G, Li H, Jiang M, Xie, et al. A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with Multi-source domains[EB/OL]. https// doi.org/10.1016/j.aei, 2021.
[17] 刘颖，雷研博，范九伦，等. 基于小样本学习的图像分类技术综述[J]. 自动化学报，2020，47(2)：297-315.
LIU Ying, LEI Yan-bo, FAN Jiu-lun, et al. Survey on image classification technology based on small sample learning[J]. Aota automatica sinice, 2020, 47(2): 297-315.
[18] XingY, Wenbo G, Yu Y. ES-MAML:Simple Hessian-Free Meta Learning[EB/OL]. http//arXiv:1910.01215, 2020.
[19] LI Z, HOIEMD. Learning without Forgetting[J]. European
conference on computer vision, 2018, 40(12): 614-629.
[20] Srinivas S, Fleuret F. Knowledge Transfer with Jacobian Matching[EB/OL].（2018-03-01）[2022-11-17] https://arxiv.org/abs/1803.00443.
[21] 王玉静，吕海岩，康守强，等. 不同型号滚动轴承故障诊断方法[J]. 中国电机工程学报，2021，41(1)：267-276.
WANG Yu-jing, LYU Hai-yan, KANG Shou-qiang, et al. Fault Diagnosis Method for Different Types of Rolling Bearings[J]. Proceedings of the CSEE, 2021, 41(1): 267-276.
[22] Van Der Maten L, Hinton G．Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(11): 2579-2625.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(2351 KB)
