Few-Shot Learning for Full Ceramic Bearing Fault Diagnosis with Acoustic Emission Signals
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Full ceramic bearings are critical components in many full ceramic and oil-free food processing and medical equipment. Developing effective full ceramic fault diagnostic methods is important. Supervised deep learning approaches have been considered promising for fault diagnosis in the era of big data where abundantly labelled datasets are available. However, in many industrial applications, datasets with fault labels are rare. This challenge has motivated the task for developing deep learning approaches for fault diagnosis with few training examples. To meet the challenge, one attractive direction is to use available pre-trained deep learning architectures to do fault diagnosis with only few examples. Specifically, this paper investigates the effectiveness of using pre-trained deep learning architectures successfully used in natural language processing to achieve few-shot learning for full ceramic bearing fault diagnosis using acoustic emission signals.
##plugins.themes.bootstrap3.article.details##
Fault Diagnosis, Full Ceramic Bearings, Few-Shot Learning
Chen, Y., Wang, Y., Liu, Y., Xu, Z., & Darrell, T. (2020). A new Meta-Baseline for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1193411943). https://doi.org/10.1109/CVPR42600.2020.01193
Chen, T., Liu, S., Kira, Z., Wang, Y., & Huang, J. (2019). A closer look at few-shot classification. In Proceedings of the International Conference on Learning Representations (ICLR). Retrieved from https://openreview.net/forum?id=HkxLXnAcFQ.
Feng, Y., Chen, J., Xie, J., Zhang, T., Lv, H., & Pan, T. (2022). Meta-learning as a promising approach for fewshot cross-domain fault diagnosis: Algorithms, applications, and prospects. Knowledge-Based Systems, 237, 107165. https://doi.org/10.1016/j.knosys.2021.107165.
Hu, S. X., Li, D., Stühmer, J., Kim, M., & Hospedales, T. M. (2019). Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1180311812). doi: 10.1109/CVPR.2019.01206
Ren, M., Zhai, D., & Yang, B. (2020). Few-shot learning with global class representations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, 9195-9204.
Chen, Q., & Li, Z. (2020). Few-shot learning with auxiliary classifier generative adversarial networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, 10660-10669.
Ko, T., Krishnamurthy, J., Aluru, S., & Chang, S. F. (2020). Learning representations for few-shot learning with deep convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2342-2355.
Lee, J. Y., & Park, J. (2021). Few-shot learning with metatransformer. IEEE Transactions on Multimedia, 23, 2573-2583.
Liu, X., Zhang, Y., Liu, Y., Li, H., & Li, Z. (2021). Metalearning as a promising approach for few-shot crossdomain fault diagnosis: Algorithms, applications, and prospects. Neurocomputing, vol. 449, pp. 93-103. https://doi.org/10.1016/j.neucom.2021.05.086.
Yan, Y., Liu, Y., Liu, X., Chen, Y., Peng, Y., & Li, X. (2021). Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation. IEEE Transactions on Industrial Informatics, vol. 17, no. 4, pp. 2834-2844.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. URL https://s3-us-west2.amazonaws.com/openai-assets/researchcovers/languageunsupervised/language_understanding_paper.pdf.
G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto, "A Baseline for Few-Shot Image Classification," in Proceedings of the International Conference on Learning Representations (ICLR), 2020.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.