Benefiting from the excellent capability of data processing, deep learning-based methods have been well applied in fault diagnosis. However, these methods may perform poorly due …
T Tang, C Qiu, T Yang, J Wang, J Zhao, M Chen, J Wu… - Measurement, 2023 - Elsevier
Recently, the progress of intelligent fault diagnosis shows deep learning-based methods with large data have achieved great success. Nevertheless, in engineering practice, limited …
J He, Z Zhu, X Fan, Y Chen, S Liu, D Chen - Symmetry, 2022 - mdpi.com
Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled training samples. However, in real industry applications, labeled data are scarce or even …
The techniques of machine learning, as well as deep learning (DL) methods, have seen a wide application in the intelligent fault diagnosis field these years. However, contemporary …
T Yu, H Guo, Y Zhu - Mobile Information Systems, 2022 - Wiley Online Library
The success of deep learning is based on a large number of tagged data, which is challenging to satisfy on many occasions. Especially in industry fault diagnosis, considering …
H Wang, J Wang, Y Zhao, Q Liu, M Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for …
T Hu, T Tang, R Lin, M Chen, S Han, J Wu - Measurement, 2020 - Elsevier
In the era of big data, various data-driven fault diagnosis algorithms, which are mainly based on traditional machine learning and deep learning, have been developed and successfully …
Y Feng, J Chen, T Zhang, S He, E Xu, Z Zhou - ISA transactions, 2022 - Elsevier
In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling …
L Wan, L Huang, J Ning, C Li, K Li - Knowledge-Based Systems, 2024 - Elsevier
In practical industrial applications, it is crucial to train a robust fault diagnosis (FD) model that can quickly adapt to new working conditions or fault modes using a few labeled fault …