Deep attention relation network: A zero-shot learning method for bearing fault diagnosis under unknown domains
Z Chen, J Wu, C Deng, X Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to
severe data distribution difference under variable working conditions, they have …
severe data distribution difference under variable working conditions, they have …
Deep causal factorization network: A novel domain generalization method for cross-machine bearing fault diagnosis
S Jia, Y Li, X Wang, D Sun, Z Deng - Mechanical Systems and Signal …, 2023 - Elsevier
Abstract Domain generalization (DG) has attracted much attention in bearing fault diagnosis
since it can generalize the prior diagnostic knowledge to invisible working conditions …
since it can generalize the prior diagnostic knowledge to invisible working conditions …
A novel knowledge transfer network with fluctuating operational condition adaptation for bearing fault pattern recognition
P Chen, Y Li, K Wang, MJ Zuo - Measurement, 2020 - Elsevier
Data-driven based intelligent fault pattern recognition methods of rolling element bearings
have made fruitful achievements in recent years. However, for real-world diagnostic …
have made fruitful achievements in recent years. However, for real-world diagnostic …
Evaluation of different bearing fault classifiers in utilizing CNN feature extraction ability
In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an
essential part of improving machine life, reducing economic losses, and avoiding safety …
essential part of improving machine life, reducing economic losses, and avoiding safety …
Learn generalization feature via convolutional neural network: A fault diagnosis scheme toward unseen operating conditions
Y Yang, J Yin, H Zheng, Y Li, M Xu, Y Chen - IEEE Access, 2020 - ieeexplore.ieee.org
In recent years, Convolutional neural networks (CNNs) have achieved start-of-art
performance in the fault diagnosis field. If there is no available information on the unseen …
performance in the fault diagnosis field. If there is no available information on the unseen …
An end-to-end intelligent fault diagnosis application for rolling bearing based on MobileNet
W Yu, P Lv - IEEE Access, 2021 - ieeexplore.ieee.org
To find out the hidden danger in the industrial production process in time, it is necessary to
monitor the health condition of the key components of the mechanical system in operation …
monitor the health condition of the key components of the mechanical system in operation …
Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning
Y Dong, H Jiang, R Yao, M Mu, Q Yang - Reliability Engineering & System …, 2024 - Elsevier
Deep learning-based fault diagnosis methods have already attained remarkable
achievements in this field. However, rolling bearing frequently operates under variable …
achievements in this field. However, rolling bearing frequently operates under variable …
Unbalanced bearing fault diagnosis under various speeds based on spectrum alignment and deep transfer convolution neural network
Bearing fault diagnosis plays a pivotal role in the safe and reliable operation of modern
mechanical systems. However, the existing fault diagnosis methods rarely deal with the …
mechanical systems. However, the existing fault diagnosis methods rarely deal with the …
Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A comparative study
W Mao, Y Liu, L Ding, Y Li - Ieee Access, 2019 - ieeexplore.ieee.org
Due to the real working conditions and data acquisition equipment, the collected working
data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal …
data of bearings are actually limited. Meanwhile, as the rolling bearing works in the normal …
A novel framework for motor bearing fault diagnosis based on multi-transformation domain and multi-source data
Through the application of deep learning and multi-sensor data, fault features can be
automatically extracted and valuable information can be integrated to tackle intricate …
automatically extracted and valuable information can be integrated to tackle intricate …