Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions
Y An, K Zhang, Y Chai, Q Liu, X Huang - Expert Systems with Applications, 2023 - Elsevier
Unsupervised domain adaptation (UDA)-based methods have made great progress in
bearing fault diagnosis under variable working conditions. However, most existing UDA …
bearing fault diagnosis under variable working conditions. However, most existing UDA …
Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions
Intelligent data-driven system prognostic methods have been popularly developed in the
recent years. Despite the promising results, most approaches assume the training and …
recent years. Despite the promising results, most approaches assume the training and …
Federated transfer learning for bearing fault diagnosis with discrepancy-based weighted federated averaging
Generally, high performance of deep learning (DL)-based machinery fault diagnosis
methods relies on abundant labeled fault samples under various working conditions, while …
methods relies on abundant labeled fault samples under various working conditions, while …
Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions
Federated learning has been receiving increasing attention in the recent years, which
improves model performance with data privacy among different clients. The intelligent fault …
improves model performance with data privacy among different clients. The intelligent fault …
A model for predicting cervical cancer using machine learning algorithms
N Al Mudawi, A Alazeb - Sensors, 2022 - mdpi.com
A growing number of individuals and organizations are turning to machine learning (ML)
and deep learning (DL) to analyze massive amounts of data and produce actionable …
and deep learning (DL) to analyze massive amounts of data and produce actionable …
Data-augmented wavelet capsule generative adversarial network for rolling bearing fault diagnosis
Y Liu, H Jiang, C Liu, W Yang, W Sun - Knowledge-Based Systems, 2022 - Elsevier
Rolling bearing fault diagnosis with limited imbalance data is significant and challenging. It
is a nice attempt to generate data for balancing datasets. In this paper, a wavelet capsule …
is a nice attempt to generate data for balancing datasets. In this paper, a wavelet capsule …
A novel transfer learning approach in remaining useful life prediction for incomplete dataset
Due to the successful implementation of intelligent data-driven approaches, these methods
are gaining remarkable attention in predicting the remaining useful life (RUL) problems …
are gaining remarkable attention in predicting the remaining useful life (RUL) problems …
Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy
Abstract Domain generalization (DG) methods have been successfully proposed to enhance
the generalization ability of the intelligent diagnosis model. However, these methods hardly …
the generalization ability of the intelligent diagnosis model. However, these methods hardly …
Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based …
Timely and effective fault detection is essential to ensure the safe and reliable operation of
wind turbines. However, due to the complex kinematic mechanisms and harsh working …
wind turbines. However, due to the complex kinematic mechanisms and harsh working …
Prior knowledge-augmented self-supervised feature learning for few-shot intelligent fault diagnosis of machines
T Zhang, J Chen, S He, Z Zhou - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Data-driven intelligent diagnosis models expect to mine the health information of machines
from massive monitoring data. However, the size of faulty monitoring data collected in …
from massive monitoring data. However, the size of faulty monitoring data collected in …