Deep transfer learning with limited data for machinery fault diagnosis
… This study focuses on the issue of machine fault diagnosis with sparse fault data. And it is
entirely impossible to only use single or several samples to train a deep network from scratch. …
entirely impossible to only use single or several samples to train a deep network from scratch. …
An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case
L Zou, Y Li, F Xu - Neurocomputing, 2020 - Elsevier
… and fault diagnosis of mechanical equipment recently. However, the amount of labeled fault
samples is limited in industrial field, also the samples … samples. Then the enhanced training …
samples is limited in industrial field, also the samples … samples. Then the enhanced training …
Gearbox fault diagnosis using a deep learning model with limited data sample
… been used for machinery fault diagnosis. However, all of these methods have flaws. It is
difficult to select the mother wavelet function for WT [38], while EMD methods suffer from mode …
difficult to select the mother wavelet function for WT [38], while EMD methods suffer from mode …
[HTML][HTML] Fault diagnosis of rotating machinery based on improved self-supervised learning method and very few labeled samples
M Wei, Y Liu, T Zhang, Z Wang, J Zhu - Sensors, 2021 - mdpi.com
… This article has proposed a novel fault diagnosis DTC-SimCLR method for rotating machinery
based on the designed transformation combination (DTC) with the developed 1-D SimCLR…
based on the designed transformation combination (DTC) with the developed 1-D SimCLR…
Transfer learning with convolutional neural networks for small sample size problem in machinery fault diagnosis
… In this section, we describe the specific procedures of the proposed machinery fault diagnosis
method, illustrate the principle of the modified TraAdaBoost algorithm, and present the …
method, illustrate the principle of the modified TraAdaBoost algorithm, and present the …
Deep fault diagnosis for rotating machinery with scarce labeled samples
… expertise, which reveal some intrinsic characteristics of fault samples. Inspired by the … a
novel Deep fault diagnosis (DFD) method for rotating machinery with scarce labeled samples by …
novel Deep fault diagnosis (DFD) method for rotating machinery with scarce labeled samples by …
Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples
… This paper proposed a new deep learning model, RWKDCAE, for rotating machinery fault
diagnosis with limited raw time-domain vibration signal. Firstly, the one-dimensional wide-…
diagnosis with limited raw time-domain vibration signal. Firstly, the one-dimensional wide-…
A review of data-driven machinery fault diagnosis using machine learning algorithms
J Cen, Z Yang, X Liu, J Xiong, H Chen - Journal of Vibration Engineering & …, 2022 - Springer
… of data scarcity and sample imbalance that often occur in fault diagnosis. However, in the
face of … of machine learning algorithms in machinery fault diagnosis are still challenging. …
face of … of machine learning algorithms in machinery fault diagnosis are still challenging. …
Limited data rolling bearing fault diagnosis with few-shot learning
… method for machinery fault diagnosis with unbalanced … the critical sample scarcity issue in
rolling bearing fault diagnosis. In this … for rolling bearing fault diagnosis with limited data. Our …
rolling bearing fault diagnosis. In this … for rolling bearing fault diagnosis with limited data. Our …
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
… , which means limited fault data can be collected. Intelligent fault diagnosis with small & …
build intelligent diagnosis models using limited machine faulty samples to achieve accurate …
build intelligent diagnosis models using limited machine faulty samples to achieve accurate …
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