Historical Information-Aided Monitoring of Few-Sample Modes in Industrial Processes With Orthogonal Transferred Projection
K Wang, X Lei, W Zhou, S Cheng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Few-sample modes are easy to appear when a new working condition is triggered in
industrial processes especially during the early stages of the new working mode. However …
industrial processes especially during the early stages of the new working mode. However …
Meta learning based residual network for industrial production quality prediction with limited data
Y Shi, Y Cao, Y Chen, L Zhang - Scientific Reports, 2024 - nature.com
Due to the challenge of collecting a substantial amount of production-quality data in real-
world industrial settings, the implementation of production quality prediction models based …
world industrial settings, the implementation of production quality prediction models based …
Prior Knowledge-Augmented Meta-Learning for Fine-Grained Fault Diagnosis
In existing fault diagnosis methods, fault categories are generally coarse-grained, which
may result in failure to precisely identify fault details. Therefore, fine-grained fault diagnosis …
may result in failure to precisely identify fault details. Therefore, fine-grained fault diagnosis …
小样本轴承故障诊断研究综述.
司伟伟， 岑健， 伍银波， 胡学良… - Journal of …, 2023 - search.ebscohost.com
随着数据时代的来临, 基于数据驱动的轴承故障诊断方法表现出了优越的性能,
但是此类方法依赖大量标记数据, 而在实际生产过程中很难收集到大量的数据 …
但是此类方法依赖大量标记数据, 而在实际生产过程中很难收集到大量的数据 …
Potentials of few-shot learning for quality monitoring in laser welding of hairpin windings
For the analysis of high-dimensional data in electromechanical manufacturing, data-driven
techniques such as deep learning show great potential. However, the vast amount of …
techniques such as deep learning show great potential. However, the vast amount of …
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
Deep-learning (DL)-based fault diagnosis (FD) approaches require a large amount of
training data, which are difficult to obtain since they are located across different entities …
training data, which are difficult to obtain since they are located across different entities …
Domain Knowledge-Guided Contrastive Learning Framework Based on Complementary Views for Fault Diagnosis With Limited Labeled Data
Y Yao, J Feng, Y Liu - IEEE Transactions on Industrial …, 2024 - ieeexplore.ieee.org
Intelligent fault diagnosis has attracted much attention in industrial processes. The difficulty
of collecting fault samples and high price of labeling data, has led to a relative scarcity of …
of collecting fault samples and high price of labeling data, has led to a relative scarcity of …
Relation Awareness Network for Few-Shot Fine-Grained Fault Diagnosis
Y Xu, X Ma, X Wang, J Wang, G Tang… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Few-shot fine-grained fault diagnosis aims at identifying faults at a fine-grained level with
limited training samples, which is challenged by subtle category differences inherent in fine …
limited training samples, which is challenged by subtle category differences inherent in fine …
Mutual Supervision of MFL Heterogeneous Signals for Insufficient Sample Defect Detection on Pipeline Safety Operation
L Jiang, H Zhang, J Liu, X Shen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Magnetic flux leakage (MFL) testing is an effective non-destructive testing (NDT) method for
pipeline safety operation, and defect detection is one of the core issues in MFL signal …
pipeline safety operation, and defect detection is one of the core issues in MFL signal …
Early fault detection for rolling bearings: A meta‐learning approach
Early fault detection (EFD) of rolling bearings aims at detecting the early symptoms of faults
by monitoring small deviations of health states. Accurate EFD enables predictive …
by monitoring small deviations of health states. Accurate EFD enables predictive …