Rotating machinery fault diagnosis based on feature extraction via an unsupervised graph neural network DOI

Jing Feng,

Shouyang Bao,

Xiaobin Xu

et al.

Applied Intelligence, Journal Year: 2023, Volume and Issue: 53(18), P. 21211 - 21226

Published: May 23, 2023

Language: Английский

Multi-sensor data fusion-enabled semi-supervised optimal temperature-guided PCL framework for machinery fault diagnosis DOI
Xingxing Jiang, Xuegang Li, Qian Wang

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 101, P. 102005 - 102005

Published: Sept. 9, 2023

Language: Английский

Citations

68
Deep transfer learning strategy in intelligent fault diagnosis of rotating machinery DOI
Shengnan Tang, Jingtao Ma,

Zhengqi Yan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108678 - 108678

Published: June 3, 2024

Language: Английский

Citations

36
Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis DOI
Chao He, Hongmei Shi, Xiaorong Liu

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 288, P. 111499 - 111499

Published: Feb. 9, 2024

Language: Английский

Citations

28
A multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery DOI
Xuefang Xu, Shuo Bao, Haidong Shao

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 60, P. 102419 - 102419

Published: Feb. 22, 2024

Language: Английский

Citations

25
Fault transfer diagnosis of rolling bearings across different devices via multi-domain information fusion and multi-kernel maximum mean discrepancy DOI
Jimeng Li,

Zhangdi Ye,

Jie Gao

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 159, P. 111620 - 111620

Published: April 17, 2024

Language: Английский

Citations

22
Semi-supervised learning for industrial fault detection and diagnosis: A systemic review DOI Creative Commons
José Miguel Ramírez‐Sanz, Jose-Alberto Maestro-Prieto, Álvar Arnaiz‐González

et al.

ISA Transactions, Journal Year: 2023, Volume and Issue: 143, P. 255 - 270

Published: Sept. 25, 2023

The automation of Fault Detection and Diagnosis (FDD) is a central task for many industries today. A myriad methods are in use, although the most recent leading contenders data-driven approaches especially Machine Learning (ML) methods. ML algorithms fall into two main categories: supervised unsupervised methods, depending on whether or not instances labeled with expected outputs. However, new approach called Semi-Supervised (SSL) has recently emerged that uses few together other unlabeled training process. This can significantly improve accuracy conventional models industrial environments where data scarce. SSL been tested as promising solution over past years several FDD problems, there have no systemic reviews this sort up until present review. In study, an attempt to organize existing literature using taxonomy van Engelen & Hoos reported. least frequently used identified considered terms different fault detection tasks their common dataset structure. Moreover, set best practices proposed conclusions work implementation under real conditions, so avoid some faults.

Language: Английский

Citations

44
Bearing fault diagnosis under various conditions using an incremental learning-based multi-task shared classifier DOI
Pengcheng Wang, Hui Xiong, Haoxiang He

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 266, P. 110395 - 110395

Published: Feb. 17, 2023

Language: Английский

Citations

39
MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios DOI
Maoyou Ye, Xiaoan Yan, Dong Jiang

et al.

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 284, P. 111294 - 111294

Published: Dec. 13, 2023

Language: Английский

Citations

38
Deep continuous convolutional networks for fault diagnosis DOI
Xufeng Huang, Tingli Xie, Jinhong Wu

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 292, P. 111623 - 111623

Published: March 7, 2024

Language: Английский

Citations

15
Incremental learning with multi-fidelity information fusion for digital twin-driven bearing fault diagnosis DOI
Xufeng Huang, Tingli Xie, Shuyang Luo

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108212 - 108212

Published: March 11, 2024

Language: Английский

Citations

14