Small sample challenge in mechanicall fault diagnosis | 机械故障诊断中的小样本问题文献追踪

在机械故障诊断领域，对智能诊断方法的研究如火如荼。基于对大量机械信号的分析，智能故障诊断方法在实验室数据上取得了很好的结果。然而，工程实际中机械设备长时间处于正常（无故障）的工作状态，能采集到的故障信号样本非常少。因此，缺乏足够的故障样本作为训练数据以支撑智能故障诊断方法的训练，这是智能诊断方法在实际应用中无法令人满意的主要原因之一。

近来，机械故障诊断中的小样本问题吸引了大量学者的目光。

Model based methods:

M. S. R. B. M. Saufi, Z. A. Bin Ahmad, M. S. Leong and M. H. Lim, "Gearbox fault diagnosis using a deep learning model with limited data sample," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2020.2967822.
关键词：stacked sparse autoencoder;
A. Zhang, S. Li, Y. Cui, W. Yang, R. Dong and J. Hu, "Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning," in IEEE Access, vol. 7, pp. 110895-110904, 2019, doi: 10.1109/ACCESS.2019.2934233.
关键词：siamese neural network; Contrastive learning;
Zhao, Bo , et al. "Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions." (2020).
关键词：Normalized CNN;

Data based methods:

Y. Ding, L. Ma, J. Ma, C. Wang and C. Lu, "A Generative Adversarial Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Small Sample Size Conditions," in IEEE Access, vol. 7, pp. 149736-149749, 2019, doi: 10.1109/ACCESS.2019.2947194.
关键词：Generative adversarial networks (GAN);
I. Martin-Diaz, D. Morinigo-Sotelo, O. Duque-Perez and R. de J. Romero-Troncoso, "Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling," in IEEE Transactions on Industry Applications, vol. 53, no. 3, pp. 3066-3075, May-June 2017, doi: 10.1109/TIA.2016.2618756.
关键词：Sampling technique;
J. Mathew, C. K. Pang, M. Luo and W. H. Leong, "Classification of Imbalanced Data by Oversampling in Kernel Space of Support Vector Machines," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4065-4076, Sept. 2018, doi: 10.1109/TNNLS.2017.2751612.
关键词：Oversampling; SVM
Q. Zhou, Y. Li, Y. Tian, "A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis"in Measurement,2020.
关键词：RNN;

Loss based methods:

C. Zhang, K. C. Tan, H. Li and G. S. Hong, "A Cost-Sensitive Deep Belief Network for Imbalanced Classification," in IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 109-122, Jan. 2019, doi: 10.1109/TNNLS.2018.2832648.
关键词：Cost-Sensitive; Deep Belief Network;
X. Dong, H. Gao, L. Guo, K. Li and A. Duan, "Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data," in IEEE Access, vol. 8, pp. 71486-71496, 2020, doi: 10.1109/ACCESS.2020.2986419.
关键词：Adaptive cost; CNN;
P. Peng, W. Zhang, Y. Zhang, "Cost sensitive active learning using bidirectional gated recurrent neural networks for imbalanced fault diagnosis"in Neurocomputing,2020.
关键词：Cost-Sensitive; active learning;

Other methods:

He, Zhiyi , et al. "Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples." Knowledge-Based Systems (2019).
关键词：Transfer learning;
M. Chen, K. Zhu, R. Wang and D. Niyato, "Active Learning Based Fault Diagnosis in Self-organizing Cellular Networks," in IEEE Communications Letters, doi: 10.1109/LCOMM.2020.2991449.
关键词：Active learning;
Zou, Lei , Y. Li , and F. Xu . "An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case." Neurocomputing (2020).
关键词：Adversarial learning;

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Small sample challenge in mechanicall fault diagnosis | 机械故障诊断中的小样本问题文献追踪

Model based methods:

Data based methods:

Loss based methods:

Other methods:

Small sample challenge in mechanicall fault diagnosis | 机械故障诊断中的小样本问题文献追踪相关推荐

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Small sample challenge in mechanicall fault diagnosis | 机械故障诊断中的小样本问题 文献追踪

Model based methods:

Data based methods:

Loss based methods:

Other methods:

Small sample challenge in mechanicall fault diagnosis | 机械故障诊断中的小样本问题 文献追踪相关推荐

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Small sample challenge in mechanicall fault diagnosis | 机械故障诊断中的小样本问题文献追踪

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