ACWGAN-GP for milling tool breakage monitoring with imbalanced data
X Li, C Yue, X Liu, J Zhou, L Wang - Robotics and Computer-Integrated …, 2024 - Elsevier
Tool breakage monitoring (TBM) during milling operations is crucial for ensuring workpiece
quality and minimizing economic losses. Under the premise of sufficient training data with a …
quality and minimizing economic losses. Under the premise of sufficient training data with a …
Domain generalization in rotating machinery fault diagnostics using deep neural networks
The past years have witnessed the successful development of intelligent machinery fault
diagnostic methods. Besides the basic data-driven fault diagnosis tasks where the training …
diagnostic methods. Besides the basic data-driven fault diagnosis tasks where the training …
Improved generative adversarial network for vibration-based fault diagnosis with imbalanced data
B Zhao, Q Yuan - Measurement, 2021 - Elsevier
Effective fault diagnosis is essential for maintaining the safe running of machine systems.
Recently, the data-driven methods have shown great potential in intelligent fault diagnosis …
Recently, the data-driven methods have shown great potential in intelligent fault diagnosis …
[HTML][HTML] A double-channel hybrid deep neural network based on CNN and BiLSTM for remaining useful life prediction
C Zhao, X Huang, Y Li, M Yousaf Iqbal - Sensors, 2020 - mdpi.com
In recent years, prognostic and health management (PHM) has played an important role in
industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the …
industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the …
A class-aware supervised contrastive learning framework for imbalanced fault diagnosis
J Zhang, J Zou, Z Su, J Tang, Y Kang, H Xu… - Knowledge-Based …, 2022 - Elsevier
Deep learning-based fault diagnosis models constructed from imbalanced datasets would
meet severe performance degradation when the number of samples for fault classes is much …
meet severe performance degradation when the number of samples for fault classes is much …
A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data
W Hou, C Zhang, Y Jiang, K Cai, Y Wang, N Li - Measurement, 2023 - Elsevier
Transfer learning exhibits exciting advantages in solving the data shortage in fault
diagnosis, while most of the existing methods still need target domain fault data, which …
diagnosis, while most of the existing methods still need target domain fault data, which …
Adaptive cost-sensitive learning: Improving the convergence of intelligent diagnosis models under imbalanced data
The natural distribution of industrial data is imbalanced, which deteriorates the performance
of intelligent fault diagnostic models. Although cost-sensitive learning is an effective method …
of intelligent fault diagnostic models. Although cost-sensitive learning is an effective method …
Imbalanced domain generalization via Semantic-Discriminative augmentation for intelligent fault diagnosis
Abstract Domain generalization-based fault diagnosis (DGFD) has garnered significant
attention due to its ability to generalize prior diagnostic knowledge to unseen working …
attention due to its ability to generalize prior diagnostic knowledge to unseen working …
Fault diagnosis of rotating machinery based on combination of Wasserstein generative adversarial networks and long short term memory fully convolutional network
Y Li, W Zou, L Jiang - Measurement, 2022 - Elsevier
The traditional fault diagnosis methods of rotating machinery based on deep learning have
made some achievements. However, the fault samples are generally difficult to collect …
made some achievements. However, the fault samples are generally difficult to collect …
Intelligent ball screw fault diagnosis using a deep domain adaptation methodology
Intelligent data-driven fault diagnosis methods have been successfully developed in the
recent years. However, as one of the most important machines in the industries, the ball …
recent years. However, as one of the most important machines in the industries, the ball …