A Dual-Constraint Centroid Contrastive Prototypical Network for Flip Chip Defect Detection Under Limited Labeled Data DOI
Yunxia Lou, Lei Su, Jiefei Gu

и другие.

Опубликована: Окт. 31, 2024

Язык: Английский

Balanced residual distillation learning for 3D point cloud class-incremental semantic segmentation DOI
Yuanzhi Su, Siyuan Chen, Yuan‐Gen Wang

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126399 - 126399

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Prior knowledge-informed multi-task dynamic learning for few-shot machinery fault diagnosis DOI
Tianci Zhang, Jinglong Chen, Zhi‐Sheng Ye

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126439 - 126439

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Acoustic emission and electromagnetic radiation precursor signal identification and early warning of coal and gas outburst based on diffusion-semi-supervised classification method DOI

Binglong Liu,

Zhonghui Li,

Zesheng Zang

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(12)

Опубликована: Дек. 1, 2024

Gas outbursts in coal seams represent a severe and formidable hazard, posing significant threat to the safety of mining operations. The advanced early warning is crucial preventive measure against outbursts. Acoustic emission (AE) electromagnetic radiation (EMR) are monitoring techniques for gas However, during operations, interference signals from AE EMR may arise. Due impact these signals, use statistical indicators time-frequency feature analysis lead false alarms missed detections outburst warnings. advancement deep learning offers new methods intelligent identification risks. This article proposes an method detecting precursor conducting comprehensive index based on EMR. First, reconstruct signal using wavelet packet decomposition then process resulting with diffusion-semi-supervised classification algorithm, employing partially labeled train model risk By analyzing prominent EMR, establish Bayesian networks, thereby achieving findings suggest that question, which employs training dataset comprising 60% manually annotated data, proficient precisely identifying adept at range signals. It provides basis distinguished multi-level warning. research outcomes significantly enhance reliability offering effective seams, power manifestations, abnormal gas.

Язык: Английский

Процитировано

3

An interpretable deep feature aggregation framework for machinery incremental fault diagnosis DOI
Kui Hu, Qian Chen, Jintao Yao

и другие.

Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103189 - 103189

Опубликована: Фев. 15, 2025

Язык: Английский

Процитировано

0

FSPDF: Few-shot learning with progressive dual-domain feature fusion via self-supervised learning DOI
Dongqing Li,

Jie Jin,

Linhua Zou

и другие.

Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113389 - 113389

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Semi-supervised Feature Contrast Incremental Learning Framework for Bearing Fault Diagnosis with Limited Labeled Samples DOI
X. Tao, Changqing Shen, Lin Li

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113172 - 113172

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Compact-sparse prototype calibration network for few-shot continual fault diagnosis of rotating machinery DOI
Yan Shen, Haidong Shao, Xinyi Wang

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 155, С. 111099 - 111099

Опубликована: Май 12, 2025

Язык: Английский

Процитировано

0

Simplicial complexes graph convolution networks with higher-order features learning for limited samples diagnosis DOI

Xian-Jie Zhang,

Haifeng Zhang, Kai Zhong

и другие.

Control Engineering Practice, Год журнала: 2025, Номер 162, С. 106391 - 106391

Опубликована: Май 13, 2025

Язык: Английский

Процитировано

0

Prototype space boundary alignment network for bearing continuous fault diagnosis under class-incremental scenarios DOI

Zhenzhong He,

Juanjuan Shi,

Weiguo Huang

и другие.

IEEE Sensors Journal, Год журнала: 2024, Номер 25(1), С. 1076 - 1085

Опубликована: Ноя. 14, 2024

Язык: Английский

Процитировано

1

Development of a Hierarchical Clustering Method for Anomaly Identification and Labelling of Marine Machinery Data DOI Creative Commons
Christian Velasco-Gallego, Iraklis Lazakis, Nieves Mateo

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1792 - 1792

Опубликована: Окт. 9, 2024

The application of artificial intelligence models for the fault diagnosis marine machinery increased expeditiously within shipping industry. This relates to effectiveness in capturing patterns systems that are becoming more complex and where traditional methods is unfeasible. However, despite these advances, lack labelling data still a major concern due confidentiality issues, appropriate data, instance. In this study, method based on histogram similarity hierarchical clustering proposed as an attempt label distinct anomalies faults occur dataset so supervised learning can then be implemented. To validate methodology, case study main engine tanker vessel considered. results indicate preliminary option classify types may appear dataset, model achieved accuracy approximately 95% presented.

Язык: Английский

Процитировано

0