A systematic review on imbalanced learning methods in intelligent fault diagnosis
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
Tackling class imbalance in computer vision: a contemporary review
Class imbalance is a key issue affecting the performance of computer vision applications
such as medical image analysis, objection detection and recognition, image segmentation …
such as medical image analysis, objection detection and recognition, image segmentation …
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 …
Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects
Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …
strong feature representation capability in recent years. Nevertheless, in engineering …
Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery
The distribution of monitored data during the service life of machinery equipment is
imbalanced, especially there is more monitoring data for health conditions than for failure …
imbalanced, especially there is more monitoring data for health conditions than for failure …
Fault diagnosis method based on two-stage GAN for data imbalance
W Luo, W Yang, J He, H Huang, H Chi… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
In the field of fault diagnosis, a large number of sensors are used to reflect the working status
of equipment with the increasing complexity of mechanical systems. However, collecting …
of equipment with the increasing complexity of mechanical systems. However, collecting …
Autoregressive data generation method based on wavelet packet transform and cascaded stochastic quantization for bearing fault diagnosis under unbalanced …
Y Sun, H Tao, V Stojanovic - Engineering Applications of Artificial …, 2024 - Elsevier
The capacity to diagnose faults in rolling bearings is of significant practical importance to
ensure the normal operation of the equipment. However, because it is challenging to obtain …
ensure the normal operation of the equipment. However, because it is challenging to obtain …
Wind turbine fault diagnosis for class-imbalance and small-size data based on stacked capsule autoencoder
Wind power is of strategic importance for reducing carbon dioxide emissions, minimizing
environmental pollution, and enhancing the sustainability of energy supply. Health …
environmental pollution, and enhancing the sustainability of energy supply. Health …
Self-contrastive Learning-optimized General Agent for long-tailed fault diagnosis of shipboard antennas leveraging adaptive data distribution
Q Cui, S He, C Hu, J Bao, Y Peng, J Chen - Measurement, 2025 - Elsevier
To address the challenges of low accuracy and limited generalization in long-tailed fault
diagnosis, an adaptive data distribution-based reinforcement learning General Agent is …
diagnosis, an adaptive data distribution-based reinforcement learning General Agent is …
Categorical feature GAN for imbalanced intelligent fault diagnosis of rotating machinery
The problem of imbalanced data amounts between the samples of healthy and faulty
conditions degrades the performance of traditional intelligent diagnosis methods for rotating …
conditions degrades the performance of traditional intelligent diagnosis methods for rotating …