Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation

X Li, W Zhang, Q Ding, JQ Sun - Journal of Intelligent Manufacturing, 2020 - Springer
… of fault. This paper proposes a deep learning approach with data augmentation for rotating
machinery fault diagnosis. … with data augmentation is proposed for fault diagnosis of rotating …

Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework

W Li, X Zhong, H Shao, B Cai, X Yang - Advanced Engineering Informatics, 2022 - Elsevier
… In order to validate the effectiveness of the proposed method, two multi-mode fault data
augmentation and intelligent diagnosis cases for bearings and gears are studied in this chapter. …

A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning

K Yu, TR Lin, H Ma, X Li, X Li - Mechanical Systems and Signal Processing, 2021 - Elsevier
data augmentation (DA) and metric learning is proposed for an intelligent bearing fault diagnosis
under limited labeled data… on an experimental bearing fault dataset from our laboratory …

Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

W Zhang, X Li, XD Jia, H Ma, Z Luo, X Li - Measurement, 2020 - Elsevier
… on two rotating machinery datasets, the data-driven fault diagnostic model can significantly
… proposed data augmentation method is promising for fault diagnostic tasks with imbalanced …

Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty

X Gao, F Deng, X Yue - Neurocomputing, 2020 - Elsevier
… eg, industrial process data, some more data augment methods are needed… data augmentation
to increase the numbers of input data samples in low-data domain of the imbalanced data

Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation

R Bai, Q Xu, Z Meng, L Cao, K Xing, F Fan - Measurement, 2021 - Elsevier
… To the best of our knowledge, in the field of rotating machinery fault diagnosis, early work …
state of equipment. In real practice, however, it is common to face the issue that the equipments

Data augmentation classifier for imbalanced fault classification

X Jiang, Z Ge - IEEE Transactions on Automation Science and …, 2020 - ieeexplore.ieee.org
… role in process monitoring and fault diagnosis. While the online monitoring system uses
sensors to obtain measurement data of mechanical equipment and industrial processes which …

Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples

M Chen, H Shao, H Dou, W Li… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
… potential for data augmentation and intelligent fault diagnosis of … Li, “Machinery fault diagnosis
with imbalanced data using … framework for rotating machinery fault diagnosis under strong …

Data preprocessing techniques in convolutional neural network based on fault diagnosis towards rotating machinery

S Tang, S Yuan, Y Zhu - IEEE Access, 2020 - ieeexplore.ieee.org
… attention of researchers in machinery fault diagnosis. In the light … of final diagnosis accuracy,
data preprocessing is necessary … data augmentation used in the CNN based intelligent fault

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
… This article reviews the research results of data-driven fault diagnosis methods of recent
years, and it includes the application status and research progress of machinery fault diagnosis