Federated zero-shot industrial fault diagnosis with cloud-shared semantic knowledge base
B Li, C Zhao - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Recently, a considerable literature has grown up around the few-sample fault diagnosis
task, in which few samples of fault data are available for model training. The lack of fault …
task, in which few samples of fault data are available for model training. The lack of fault …
MJAR: A novel joint generalization-based diagnosis method for industrial robots with compound faults
Compound faults inevitably occur in multi-joint industrial robots resulting in excessive
vibration. Intelligent diagnosis for the occurrence and position of fault joints can efficiently …
vibration. Intelligent diagnosis for the occurrence and position of fault joints can efficiently …
A novel prototype-assisted contrastive adversarial network for weak-shot learning with applications: Handling weakly labeled data
This article is concerned with weak-shot learning, a practical yet challenging scenario in
transfer learning where only a limited amount of weakly labeled data are available in the …
transfer learning where only a limited amount of weakly labeled data are available in the …
A novel Brownian correlation metric prototypical network for rotating machinery fault diagnosis with few and zero shot learners
J Yang, C Wang - Advanced Engineering Informatics, 2022 - Elsevier
Due to the variability of working conditions and the scarcity of fault samples, the existing
diagnosis models still have a big gap under the condition of covering more practical …
diagnosis models still have a big gap under the condition of covering more practical …
Adversarial domain adaptation network with pseudo-siamese feature extractors for cross-bearing fault transfer diagnosis
The traditional domain adaptation model just uses a single (siamese) feature extractor for
mapping the source domain and target domain data to a feature space simultaneously, but it …
mapping the source domain and target domain data to a feature space simultaneously, but it …
Meta-Learning With Distributional Similarity Preference for Few-Shot Fault Diagnosis Under Varying Working Conditions
Few-shot fault diagnosis is a challenging problem for complex engineering systems due to
the shortage of enough annotated failure samples. This problem is increased by varying …
the shortage of enough annotated failure samples. This problem is increased by varying …
A robust fault classification method for streaming industrial data based on Wasserstein generative adversarial network and semi-supervised ladder network
C Zhang, K Peng, J Dong, X Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of modern information technology, the collection, storage, and
transmission of information in the process industry have been gaining popularity. However …
transmission of information in the process industry have been gaining popularity. However …
Fault diagnosis of complex industrial systems based on multi-granularity dictionary learning and its application
Nowadays, the intelligent fault diagnosis problem of modern industrial systems has received
increasing attention. However, with the increasing scale of industrial systems, the same …
increasing attention. However, with the increasing scale of industrial systems, the same …
PLURAL: 3D point cloud transfer learning via contrastive learning with augmentations
Unlocking the power of 3D point cloud machine learning models can be a challenge due to
the need for extensive labeled datasets, which presents a challenge when applying these …
the need for extensive labeled datasets, which presents a challenge when applying these …
Reweighted regularized prototypical network for few-shot fault diagnosis
In this article, we study the challenging few-shot fault diagnosis (FSFD) problem where
limited faulty samples are available. Metric-based meta-learning methods have been a …
limited faulty samples are available. Metric-based meta-learning methods have been a …