| Citation: | WANG J H，LIU Q W，CAO J，et al. Fault diagnosis of gearbox with small-sample based on SCACGAN[J]. Journal of Beijing University of Aeronautics and Astronautics，2026，52（3）：713-723 （in Chinese）
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A new method for gearbox fault diagnosis based on the self-correcting auxiliary classifier generative adversarial networks (SCACGAN) is suggested in response to the limited diversity and low quality of fault samples produced by the auxiliary classifier generative adversarial networks (ACGAN) during the small-sample gearbox fault diagnosis process, which subsequently results in low diagnostic accuracy. Firstly, an independent classifier is introduced into the auxiliary classifier generative adversarial network to mitigate the adverse impact of discriminator output errors on the quality of generated samples, and to classify the health status of different gearbox samples. Secondly, the problem of low-quality generated samples during the training phase is addressed by using the least squares function to improve the model’s generation and classification skills. Lastly, a self-correcting convolutional neural network is integrated into the generator to enhance the capability of fault feature acquisition. Experimental results demonstrate that under small-sample conditions, the proposed approach is capable of generating higher-quality fault samples, thereby improving the accuracy of gearbox fault diagnosis.
| [1] |
ZHANG M, LI D C, WANG K S, et al. An adaptive order-band energy ratio method for the fault diagnosis of planetary gearboxes[J]. Mechanical Systems and Signal Processing, 2022, 165: 108336.
|
| [2] |
XUE S, HOWARD I, WANG C S, et al. Dynamic modelling of the gear system under non-stationary conditions using the iterative convergence of the tooth mesh stiffness[J]. Engineering Failure Analysis, 2022, 131: 105908.
|
| [3] |
RUAN D W, WANG J, YAN J P, et al. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis[J]. Advanced Engineering Informatics, 2023, 55: 101877.
|
| [4] |
JIA L S, CHOW T W S, YUAN Y X. GTFE-Net: a gramian time frequency enhancement CNN for bearing fault diagnosis[J]. Engineering Applications of Artificial Intelligence, 2023, 119: 105794.
|
| [5] |
WANG S D, LIU Z B, JIA Z, et al. Composite fault diagnosis of analog circuit system using chaotic game optimization-assisted deep ELM-AE[J]. Measurement, 2022, 202: 111826.
|
| [6] |
YANG D G, KARIMI H R, SUN K K. Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples[J]. Neural Networks, 2021, 141: 133-144.
|
| [7] |
LEI Y X, KARIMI H R, CHEN X F. A novel self-supervised deep LSTM network for industrial temperature prediction in aluminum processes application[J]. Neurocomputing, 2022, 502: 177-185.
|
| [8] |
ZHANG S Y, QIU T. Semi-supervised LSTM ladder autoencoder for chemical process fault diagnosis and localization[J]. Chemical Engineering Science, 2022, 251: 117467.
|
| [9] |
JIN Z Z, HE D Q, WEI Z X. Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN[J]. Engineering Applications of Artificial Intelligence, 2022, 110: 104713.
|
| [10] |
GAO S Z, XU L T, ZHANG Y M, et al. Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN[J]. ISA Transactions, 2022, 128: 485-502.
|
| [11] |
LI Y J, WANG Y B, ZHANG Y, et al. Diagnosis of inter-turn short circuit of permanent magnet synchronous motor based on deep learning and small fault samples[J]. Neurocomputing, 2021, 442: 348-358.
|
| [12] |
MAO W T, FENG W S, LIU Y M, et al. A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 150: 107233.
|
| [13] |
WANG Z S, XUAN J P. Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm[J]. Advanced Engineering Informatics, 2021, 49: 101315.
|
| [14] |
HE Z Y, SHAO H D, DING Z Y, et al. Modified deep autoencoder driven by multisource parameters for fault transfer prognosis of aeroengine[J]. IEEE Transactions on Industrial Electronics, 2021, 69(1): 845-855.
|
| [15] |
ZHOU F N, YANG S, FUJITA H, et al. Deep learning fault diagnosis method based on global optimization GAN for unbalanced data[J]. Knowledge-Based Systems, 2020, 187: 104837.
|
| [16] |
DU Z M, CHEN K, CHEN S L, et al. Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system[J]. Energy and Buildings, 2023, 289: 113072.
|
| [17] |
DU W Y, YANG J P, MENG G L. Fault diagnosis for dynamic system based on the independent latent space reconstruction of generative adversarial network[J]. Journal of Process Control, 2023, 125: 28-40.
|
| [18] |
ODENA A, OLAH C, SHLENS J. Conditional image synthesis with auxiliary classifier gans[C]//Proceedings of the International Conference on Machine Learning. Sydney: PMLR, 2017: 2642-2651.
|
| [19] |
SUN Q, PENG F, YU X H, et al. Data augmentation strategy for power inverter fault diagnosis based on Wasserstein distance and auxiliary classification generative adversarial network[J]. Reliability Engineering & System Safety, 2023, 237: 109360.
|
| [20] |
许小伟, 韦道明, 严运兵, 等. 基于改进ACGAN的永磁同步电机数据扩张方法[J]. 哈尔滨工业大学学报, 2023, 55(10): 114-121.
XU X W, WEI D M, YAN Y B, et al. Data expansion method of permanent magnet synchronous motor based on improved ACGAN[J]. Journal of Harbin Institute of Technology, 2023, 55(10): 114-121(in Chinese).
|
| [21] |
李慧芳, 徐光浩, 黄双喜. 基于主动生成式过采样和深度堆叠网络的轴承故障诊断[J]. 计算机集成制造系统, 2023, 29(1): 146-159.
LI H F, XU G H, HUANG S X. Active generative oversampling and deep stacking network based bearing fault diagnosis approach[J]. Computer Integrated Manufacturing Systems, 2023, 29(1): 146-159 (in Chinese).
|
| [22] |
MAO X D, LI Q, XIE H R, et al. Least squares generative adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 2813-2821.
|
| [23] |
LEI J H, LIU C, JIANG D X. Fault diagnosis of wind turbine based on long short-term memory networks[J]. Renewable Energy, 2019, 133: 422-432.
|
| [24] |
LI Y, ZHANG Y, LIU W, et al. A fault pattern and convolutional neural network based single-phase earth fault identification method for distribution network [C]//Proceedings of the IEEE Innovative Smart Grid Technologies-Asia. Piscataway: IEEE press, 2019: 838-843.
|
| [25] |
MIRZA M, OSINDERO S. Conditional generative adversarial nets[EB/OL]. (2014-12-06)[2023-12-16]. https://arxiv.org/abs/1411.1784.
|
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