A systematic review on imbalanced learning methods in intelligent fault diagnosis

Z Ren, T Lin, K Feng, Y Zhu, Z Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

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
Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …

[HTML][HTML] A survey on fault diagnosis approaches for rolling bearings of railway vehicles

G Yan, J Chen, Y Bai, C Yu, C Yu - Processes, 2022 - mdpi.com
This paper reviews the current research status of rolling bearing fault diagnosis technology
for railway vehicles. Several domains are covered, including vibration fault diagnosis …

Bearing fault diagnosis using time segmented Fourier synchrosqueezed transform images and convolution neural network

SK Gundewar, PV Kane - Measurement, 2022 - Elsevier
In this paper, a time segmented Fourier synchro-squeezed transform-based convolution
neural network is proposed for the bearing fault diagnosis. The proposed method acquired …

A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects

M Gao, P Song, F Wang, J Liu, A Mandelis… - Journal of …, 2021 - Wiley Online Library
Wood defects are quickly identified from an optical image based on deep learning
methodology, which effectively improves wood utilization. Traditional neural network …

[HTML][HTML] CNN-based defect inspection for injection molding using edge computing and industrial IoT systems

H Ha, J Jeong - Applied Sciences, 2021 - mdpi.com
Currently, the development of automated quality inspection is drawing attention as a major
component of the smart factory. However, injection molding processes have not received …

CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis

C Lian, Y Zhao, J Shao, T Sun, F Dong, Z Ju, Z Zhan… - Information …, 2024 - Elsevier
Data-driven fault diagnosis has become a hot topic of research in recent years, due to its
wide applicability, high accuracy, and ease of modeling. In data-driven fault diagnosis …

A rolling bearing fault diagnosis method using novel lightweight neural network

D He, C Liu, Y Chen, Z Jin, X Li… - … Science and Technology, 2021 - iopscience.iop.org
As an important part of rotating machinery, rolling bearing fault will lead to equipment fault,
resulting in loss of property and personal safety. To overcome the deficiency of traditional …

[HTML][HTML] Leveraging label information in a knowledge-driven approach for rolling-element bearings remaining useful life prediction

T Berghout, M Benbouzid, LH Mouss - Energies, 2021 - mdpi.com
Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios,
data-driven research has been directed towards recovering them by imposing accelerated …

[HTML][HTML] Few-shot learning-based light-weight WDCNN model for bearing fault diagnosis in siamese network

D Lee, J Jeong - Sensors, 2023 - mdpi.com
In this study, bearing fault diagnosis is performed with a small amount of data through few-
shot learning. Recently, a fault diagnosis method based on deep learning has achieved …