Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions
The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
artificial intelligence-related technologies. In engineering scenarios, machines usually work …
Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook
The advent of the Industry 4.0 initiative has made it so that manufacturing environments are
becoming more and more dynamic, connected but also inherently more complex, with …
becoming more and more dynamic, connected but also inherently more complex, with …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
Limited data rolling bearing fault diagnosis with few-shot learning
This paper focuses on bearing fault diagnosis with limited training data. A major challenge in
fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type …
fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type …
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 …
An intelligent fault diagnosis model based on deep neural network for few-shot fault diagnosis
C Wang, Z Xu - Neurocomputing, 2021 - Elsevier
The most existing deep neural networks (DNN)-based methods for fault diagnosis only focus
on prediction accuracy without considering the limitation of labeled sample size. In practical …
on prediction accuracy without considering the limitation of labeled sample size. In practical …
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 …
Diagnosis of interturn short-circuit faults in permanent magnet synchronous motors based on few-shot learning under a federated learning framework
J Zhang, Y Wang, K Zhu, Y Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
A large amount of labeled data are important to enhance the performance of deep-learning-
based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality …
based methods in the area of fault diagnosis. Because it is difficult to obtain high-quality …
An interpretable data augmentation scheme for machine fault diagnosis based on a sparsity-constrained generative adversarial network
Vibration signal-based methods have been widely utilized in machine fault diagnosis.
Usually, a lack of sufficient training data can prevent these methods from achieving …
Usually, a lack of sufficient training data can prevent these methods from achieving …