Citation: | Xinyao Xu, De Xu, Fangbo Qin. A New Diagnosis Method with Few-shot Learning Based on a Class-rebalance Strategy for Scarce Faults in Industrial Processes. Machine Intelligence Research, vol. 20, no. 4, pp.583-594, 2023. https://doi.org/10.1007/s11633-022-1363-y |
[1] |
C. H. Hu, J. Y. Luo, X. Y. Kong, X. W. Feng. Novel fault subspace extraction methods for the reconstruction-based fault diagnosis. Journal of Process Control, vol. 105, pp. 129–140, 2021. DOI: 10.1016/j.jprocont.2021.07.008.
|
[2] |
P. Zhou, R. Y. Zhang, J. Xie, J. P. Liu, H. Wang, T. Y. Chai. Data-driven monitoring and diagnosing of abnormal furnace conditions in blast furnace ironmaking: An integrated PCA-ICA method. IEEE Transactions on Industrial Electronics, vol. 68, no. 1, pp. 622–631, 2021. DOI: 10.1109/TIE.2020.2967708.
|
[3] |
L. Wen, X. Y. Li, L. Gao, Y. Y. Zhang. A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5990–5998, 2018. DOI: 10.1109/TIE.2017.2774777.
|
[4] |
H. Liu, J. Z. Zhou, Y. Zheng, W. Jiang, Y. C. Zhang. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Transactions, vol. 77, pp. 167–178, 2018. DOI: 10.1016/j.isatra.2018.04.005.
|
[5] |
S. Yin, S. X. Ding, X. C. Xie, H. Luo. A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6418–6428, 2014. DOI: 10.1109/TIE.2014.2301773.
|
[6] |
N. Laouti, S. Othman, M. Alamir, N. Sheibat-Othman. Combination of model-based observer and support vector machines for fault detection of wind turbines. International Journal of Automation and Computing, vol. 11, no. 3, pp. 274–287, 2014. DOI: 10.1007/s11633-014-0790-9.
|
[7] |
Y. Zhang, C. Bingham, M. Garlick, M. Gallimore. Applied fault detection and diagnosis for industrial gas turbine systems. International Journal of Automation and Computing, vol. 14, no. 4, pp. 463–473, 2017. DOI: 10.1007/s11633-016-0967-5.
|
[8] |
H. Q. Wang, Y. L. Ke, G. G. Luo, G. Tang. Compressed sensing of roller bearing fault based on multiple down-sampling strategy. Measurement Science and Technology, vol. 27, no. 2, Article number 025009, 2016. DOI: 10.1088/0957-0233/27/2/025009.
|
[9] |
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. DOI: 10.1613/jair.953.
|
[10] |
J. Mathew, C. K. Pang, M. Luo, W. H. Leong. Classification of imbalanced data by oversampling in kernel space of support vector machines. IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4065–4076, 2018. DOI: 10.1109/TNNLS.2017.2751612.
|
[11] |
H. B. He, Y. Bai, E. A. Garcia, S. T. Li. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In Proceedings of IEEE International Joint Conference on Neural Networks, Hong Kong, China, pp. 1322-1328, 2008. DOI: 10.1109/IJCNN.2008.4633969.
|
[12] |
X. X. Wu, Y. G. He, J. J. Duan. A deep parallel diagnostic method for transformer dissolved gas analysis. Applied Sciences, vol. 10, no. 4, Article number 1329, 2020. DOI: 10.3390/app10041329.
|
[13] |
Q. W. Guo, Y. B. Li, Y. Song, D. C. Wang, W. Chen. Intelligent fault diagnosis method based on full 1-D convolutional generative adversarial network. IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2044–2053, 2020. DOI: 10.1109/TII.2019.2934901.
|
[14] |
Y. Zhuo, Z. Q. Ge. Auxiliary information-guided industrial data augmentation for any-shot fault learning and diagnosis. IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7535–7545, 2021. DOI: 10.1109/TII.2021.3053106.
|
[15] |
A. Odena, C. Olah, J. Shlens. Conditional image synthesis with auxiliary classifier GANs. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, pp. 2642–2651, 2017.
|
[16] |
Z. Y. Wu, W. F. Lin, Y. Ji. An integrated ensemble learning model for imbalanced fault diagnostics and prognostics. IEEE Access, vol. 6, pp. 8394–8402, 2018. DOI: 10.1109/ACCESS.2018.2807121.
|
[17] |
L. J. Zhou, J. W. Dang, Z. H. Zhang. Fault classification for on-board equipment of high-speed railway based on attention capsule network. International Journal of Automation and Computing, vol. 18, no. 5, pp. 814–825, 2021. DOI: 10.1007/s11633-021-1291-2.
|
[18] |
Z. X. Hu, P. Jiang. An imbalance modified deep neural network with dynamical incremental learning for chemical fault diagnosis. IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 540–550, 2019. DOI: 10.1109/TIE.2018.2798633.
|
[19] |
W. K. Yu, C. H. Zhao. Broad convolutional neural network based industrial process fault diagnosis with incremental learning capability. IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 5081–5091, 2020. DOI: 10.1109/TIE.2019.2931255.
|
[20] |
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, S. Wermter. Continual lifelong learning with neural networks: A review. Neural Networks, vol. 113, pp. 54–71, 2019. DOI: 10.1016/j.neunet.2019.01.012.
|
[21] |
J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, F. F. Li, ImageNet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 248–255, 2009. DOI: 10.1109/CVPR.2009.5206848.
|
[22] |
J. Xu, P. F. Xu, Z. C. Wei, X. Ding, L. Shi. DC-NNMN: Across components fault diagnosis based on deep few-shot learning. Shock and Vibration, vol. 2020, Article number 3152174, 2020. DOI: 10.1155/2020/3152174.
|
[23] |
C. J. Li, S. B. Li, A. S. Zhang, Q. He, Z. H. Liao, J. J. Hu. Meta-learning for few-shot bearing fault diagnosis under complex working conditions. Neurocomputing, vol. 439, pp. 197–211, 2021. DOI: 10.1016/j.neucom.2021.01.099.
|
[24] |
A. S. Zhang, S. B. Li, Y. X. Cui, W. L. Yang, R. Z. Dong, J. J. Hu. Limited data rolling bearing fault diagnosis with few-shot learning. IEEE Access, vol. 7, pp. 110895–110904, 2019. DOI: 10.1109/ACCESS.2019.2934233.
|
[25] |
N. Lu, H. Y. Hu, T. Yin, Y. G. Lei, S. H. Wang. Transfer relation network for fault diagnosis of rotating machinery with small data. IEEE Transactions on Cybernetics, to be published. DOI: 10.1109/TCYB.2021.3085476.
|
[26] |
C. Finn, P. Abbeel, S. Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, pp. 1126–1135, 2017.
|
[27] |
D. Wang, M. Zhang, Y. C. Xu, W. N. Lu, J. Yang, T. Zhang. Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions. Mechanical Systems and Signal Processing, vol. 155, Article number 107510, 2021. DOI: 10.1016/j.ymssp.2020.107510.
|
[28] |
J. Snell, K. Swersky, R. Zemel. Prototypical networks for few-shot learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, pp. 4080−4090, 2017.
|
[29] |
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, D. Wierstra. Matching networks for one shot learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, pp. 3637–3645, 2016.
|
[30] |
G. Koch, R. Zemel, R. Salakhutdinov. Siamese neural networks for one-shot image recognition. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015.
|
[31] |
D. W. Li, Y. J. Tian. Survey and experimental study on metric learning methods. Neural Networks, vol. 105, pp. 447–462, 2018. DOI: 10.1016/j.neunet.2018.06.003.
|
[32] |
F. Schroff, D. Kalenichenko, J. Philbin. FaceNet: A unified embedding for face recognition and clustering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, pp. 815-823, 2015. DOI: 10.1109/CVPR.2015.7298682.
|
[33] |
J. J. Downs, E. F. Vogel. A plant-wide industrial process control problem. Computers &Chemical Engineering, vol. 17, no. 3, pp. 245–255, 1993. DOI: 10.1016/0098-1354(93)80018-I.
|
[34] |
A. Bathelt, N. L. Ricker, M. Jelali. Revision of the Tennessee Eastman process model. IFAC-PapersOnLine, vol. 48, no. 8, pp. 309–314, 2015. DOI: 10.1016/j.ifacol.2015.08.199.
|
[35] |
L. Van der Maaten, G. Hinton. Visualizing data using t-SNE. Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579–2605, 2008.
|