Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects DOI
Hanlin Guan, Yan Ren, Hesheng Tang

et al.

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 35(8), P. 082001 - 082001

Published: April 25, 2024

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely accurately fault diagnosis hydraulic components is helpful to curb economic losses accidents, so researches carried out a lot research on components. Information fusion technology can combine multi-source data from multiple dimensions mine features, which effectively improves accuracy reliability results. However, there currently lack comprehensive systematic review in this domain. Therefore, paper, information technologies are summarized analyzed, encompassing main process status system. The methods techniques involved process, source method elaborated summarized. problems solutions discussed, ideas improving put forward. Finally, digital twin (DT) introduced, advantages intelligent based DT On basis, summarized, challenges future applying forward analyzed comprehensively.

Language: Английский

Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis DOI
Jun Wang, He Ren, Changqing Shen

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 243, P. 109879 - 109879

Published: Dec. 9, 2023

Language: Английский

Citations

45

Dynamic normalization supervised contrastive network with multiscale compound attention mechanism for gearbox imbalanced fault diagnosis DOI
Yutong Dong, Hongkai Jiang, Wenxin Jiang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108098 - 108098

Published: March 8, 2024

Language: Английский

Citations

34

Digital twins in safety analysis, risk assessment and emergency management DOI Creative Commons
Enrico Zio, Leonardo Miqueles

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 246, P. 110040 - 110040

Published: Feb. 25, 2024

Digital twins (DTs) represent an emerging technology that is currently leveraging the monitoring of complex systems, implementation autonomous control and assistance during accidents emergencies in real time. However, aspects such as safety, cybersecurity reliability DTs are still open issues have not been comprehensively addressed. These can offer new insights to evaluate risk return obtained from DTs. This paper presents a systematic literature review focused on their use safety analysis, assessment emergency management. The aim this work twofold: (i) point at latest advancements by presenting catalog expected functions twinning enabling technologies application domains interest; (ii) limitations pending challenges for

Language: Английский

Citations

31

Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis DOI
Yutong Dong, Hongkai Jiang, Mingzhe Mu

et al.

Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102573 - 102573

Published: May 2, 2024

Language: Английский

Citations

26

Self-paced decentralized federated transfer framework for rotating machinery fault diagnosis with multiple domains DOI
Ke Zhao, Zhenbao Liu, Jia Li

et al.

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 211, P. 111258 - 111258

Published: Feb. 21, 2024

Language: Английский

Citations

18

An interpretable multiscale lifting wavelet contrast network for planetary gearbox fault diagnosis with small samples DOI
Yutong Dong, Hongkai Jiang, Xin Wang

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: 251, P. 110404 - 110404

Published: July 28, 2024

Language: Английский

Citations

17

Multi-scale dynamic graph mutual information network for planet bearing health monitoring under imbalanced data DOI

Wenbin Cai,

Dezun Zhao, Tianyang Wang

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 64, P. 103096 - 103096

Published: Jan. 5, 2025

Language: Английский

Citations

5

Dynamic weighted adversarial domain adaptation network with sparse representation denoising module for rotating machinery fault diagnosis DOI
Maogui Niu, Hongkai Jiang, Haidong Shao

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 142, P. 109963 - 109963

Published: Jan. 5, 2025

Language: Английский

Citations

2

Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning DOI
Yutong Dong, Hongkai Jiang, Renhe Yao

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 243, P. 109805 - 109805

Published: Nov. 10, 2023

Language: Английский

Citations

32

Surrogate modeling of pantograph-catenary system interactions DOI
Cheng Yao, Jingke Yan, Fan Zhang

et al.

Mechanical Systems and Signal Processing, Journal Year: 2024, Volume and Issue: 224, P. 112134 - 112134

Published: Dec. 3, 2024

Language: Английский

Citations

14