| Citation: | MA X，XU S，SHANG P C，et al. Fault diagnosis of gearbox under open set and cross working condition based on transfer learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（5）：1753-1760 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0719
|
With the continuous development of industry and aerospace technology, the working conditions and failure modes of rotating machinery are becoming increasingly diversified and complex, and reliability and safety problems are becoming increasingly prominent. It is critical to research efficient fault detection techniques since many working condition data lack fault labels and the failure modes amongst various working conditions are asymmetric. Take the gearbox as the case verification object, set up cross-working conditions, and open set fault diagnosis scenarios. A method is proposed to address the issue of lacking fault labels under the target working condition. It takes into account the ability of migration learning to facilitate cross-domain knowledge application. Specifically, migration learning is used to transfer knowledge from the source working condition to the target working condition, and the cross entropy classification loss function is used to identify known fault types. However, transfer learning has the problem that the greater the field difference is, the worse the effect is. It is difficult to solve the open set problem of asymmetric fault modes under cross-working conditions. In order to identify the known and unknown classes of target working circumstances, a method utilizing a convolutional neural network to extract similar data characteristics between working conditions is proposed. The two classification loss functions are then used in this process. The joint loss function is proposed to train the diagnosis model and realize the joint migration of fault features from the source domain to the target domain. The results of the case analysis show that the method can realize cross-working condition fault diagnosis under an open set, and the average diagnostic accuracy is more than 90%.
| [1] |
SIAHPOUR S, LI X, LEE J. Deep learning-based cross-sensor domain adaptation for fault diagnosis of electro-mechanical actuators[J]. International Journal of Dynamics and Control, 2020, 8: 1054-1062. doi: 10.1007/s40435-020-00669-0
|
| [2] |
MOSLEM A, JASKARAN S, LI X, et al. Cross-domain gearbox diagnostics under variable working conditions with deep convolutional transfer learning[J]. Journal of Vibration and Control, 2020, 27(7-8): 854-864.
|
| [3] |
ZHANG W, LI X, MA H, et al. Open set domain adaptation in machinery fault diagnostics using instance-level weighted adversarial learning[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7445-7455. doi: 10.1109/TII.2021.3054651
|
| [4] |
陈宏, 邓芳明, 吴翔, 等. 基于梯度提升决策树的电力电子电路故障诊断[J]. 测控技术, 2017, 36(5): 9-12. doi: 10.3969/j.issn.1000-8829.2017.05.003
CHEN H, DENG F M, WU X, et al. Fault diagnosis of power electronics circuit based on gradient boosting decision tree[J]. Measurement & Control Technology, 2017, 36(5): 9-12(in Chinese). doi: 10.3969/j.issn.1000-8829.2017.05.003
|
| [5] |
王廷轩, 刘韬, 王振亚, 等. 融合迁移卷积神经网络的跨域滚动轴承故障诊断[J]. 电子测量技术, 2021, 44(10): 167-174.
WANG T X, LIU T, WANG Z Y, et al. Cross-domain rolling bearing fault diagnosis based on convolutional neural network combined with transfer learning[J]. Electronic Measurement Technology, 2021, 44(10): 167-174(in Chinese).
|
| [6] |
洪晓翠, 段礼祥, 杨晓光, 等. 智能优化算法在机械故障诊断领域 的应用综述[J]. 测控技术, 2021, 40(7): 1-8.
HONG X C, DUAN L X, YANG X G, et al. Review on the application of intelligent optimization algorithms in mechanical fault diagnosis[J]. Measurement & Control Technology, 2021, 40(7): 1-8(in Chinese).
|
| [7] |
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 G B, LI H, RAN Y, et al. A transfer learning model for bearing fault diagnosis[J]. Journal of Jilin University (Engineering and Technology Edition), 2020, 50(5): 1617-1626(in Chinese).
|
| [8] |
刘应东, 刘韬, 李华, 等. 变工况轴承的联合分布适应迁移故障诊断[J]. 电子测量与仪器学报, 2021, 35(5): 69-75.
LIU Y D, LIU T, LI H, et al. Transfer fault diagnosis of bearings under variable working conditions based on joint distribution adaptation[J]. Journal of Electronic Measurement and Instrumentation, 2021, 35(5): 69-75(in Chinese).
|
| [9] |
毕爽. 基于深度迁移学习的风电机组齿轮箱故障诊断方法研究[D]. 长春: 长春大学, 2021.
BI S. Research on fault diagnosis method of wind turbine gearbox based on deep transfer learning[D]. Changchun: Changchun University, 2021(in Chinese).
|
| [10] |
陈仁祥, 杨星, 胡小林, 等. 深度置信网络迁移学习的行星齿轮箱故障诊断方法[J]. 振动与冲击, 2021, 40(1): 127-133.
CHEN R X, YANG X, HU X L, et al. Planetary gearbox fault diagnosis method based on deep belief network transfer learning[J]. Journal of Vibration and Shock, 2021, 40(1): 127-133(in Chinese).
|
| [11] |
CAO P, ZHANG S L, TANG J. Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning[J]. IEEE Access, 2018, 6: 26241-26253. doi: 10.1109/ACCESS.2018.2837621
|
| [12] |
MA P, ZHANG H L, FAN W H, et al. A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network[J]. Measurement Science and Technology, 2019, 30: 055402. doi: 10.1088/1361-6501/ab0793
|
| [13] |
CHEN Z Y, HE G L, LI J P, et al. Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(11): 8702-8712. doi: 10.1109/TIM.2020.2995441
|
| [14] |
ZHAO C, SHEN W M. Dual adversarial network for cross-domain open set fault diagnosis[J]. Reliability Engineering & System Safety, 2022, 221: 108358.
|
| [15] |
文成林, 吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报, 2020, 42(1): 234-248. doi: 10.11999/JEIT190715
WEN C L, LV F Y. Review on deep learning based fault diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(1): 234-248(in Chinese). doi: 10.11999/JEIT190715
|
| [16] |
CHEN W Q, QIU Y N, FENG Y H, et al. Diagnosis of wind tur-bine faults with transfer learning algorithms[J]. Renewable Energy, 2021, 163: 2053-2067. doi: 10.1016/j.renene.2020.10.121
|
| [17] |
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): 1-18.
|
| [18] |
BUSTO P P, GALL J. Open set domain adaptation[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 754-763.
|
| [19] |
LI X, ZHANG W, DING Q, et al. Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation[J]. Journal of Intelligent Manufacturing, 2020, 31(2): 433-452. doi: 10.1007/s10845-018-1456-1
|
| [20] |
TANG S N, YUAN S Q, ZHU Y. Deep learning-based intelligent fault diagnosis methods toward rotating machinery[J]. IEEE Access, 2020, 8: 9335-9346. doi: 10.1109/ACCESS.2019.2963092
|
| [21] |
LI F, TANG T J, TANG B P, et al. Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings[J]. Measurement, 2021, 169: 108339. doi: 10.1016/j.measurement.2020.108339
|
| [22] |
郭瑜, 童丽娜, 倪旭明. 基于改进卷积神经网络的电力通信网故障诊断研究[J]. 计算机测量与控制, 2022, 30(2): 24-30.
GUO Y, TONG L N, YAN X M. Research on fault diagnosis of electric power communication network based on improved convolutional neural network[J]. Computer Measurement & Control, 2022, 30(2): 24-30(in Chinese).
|
Figures(5) / Tables(2)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
