Cited by A leakage monitoring technology for buried hydrogen-doped natural gas pipelines based on vibration signal with machine learning

Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning DOI

Yubo Bi,

Qiulan Wu, Shilu Wang

et al.

Energy, Journal Year: 2023, Volume and Issue: 284, P. 129361 - 129361

Published: Oct. 13, 2023

Language: Английский

Citations

Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model DOI

Xingyuan Miao, Hong Zhao,

Boxuan Gao

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 238, P. 109486 - 109486

Published: June 30, 2023

Language: Английский

Citations

Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm DOI

Hongyu Chen,

Qiping Geoffrey Shen,

Mirosław J. Skibniewski

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102705 - 102705

Published: Sept. 1, 2024

Language: Английский

Citations

Technological progress accelerates CO2 emissions peaking in a megacity: Evidence from Shanghai, China DOI

Wei Li, Zhenjie Chen, Manchun Li

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106150 - 106150

Published: Jan. 1, 2025

Language: Английский

Citations

Application of machine learning to leakage detection of fluid pipelines in recent years: A review and prospect DOI

Jianwu Chen,

Xiao Wu, Zhibo Jiang

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116857 - 116857

Published: Jan. 1, 2025

Language: Английский

Citations

Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model DOI

Xingyuan Miao, Hong Zhao

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 237, P. 109369 - 109369

Published: May 4, 2023

Language: Английский

Citations

Advanced transformer model for simultaneous leakage aperture recognition and localization in gas pipelines DOI

Pengyu Li, Xiufang Wang, Chunlei Jiang

et al.

Reliability Engineering & System Safety, Journal Year: 2023, Volume and Issue: 241, P. 109685 - 109685

Published: Sept. 24, 2023

Language: Английский

Citations

Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network DOI

Jing Huang, Zhifen Zhang, Rui Qin

et al.

Journal of Manufacturing Systems, Journal Year: 2023, Volume and Issue: 72, P. 93 - 103

Published: Nov. 28, 2023

Language: Английский

Citations

An optimized back propagation neural network on small samples spectral data to predict nitrite in water DOI

Cailing Wang, Guohao Zhang, J. Yan

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 247, P. 118199 - 118199

Published: Jan. 19, 2024

Language: Английский

Citations

Fuzzy Fault Tree Analysis and Safety Countermeasures for Coal Mine Ground Gas Transportation System DOI

Chun Liu, Jinshi Li, Di Zhang

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(2), P. 344 - 344

Published: Feb. 6, 2024

The coal mine ground gas transportation system is widely used for and mixing preheating in the storage oxidation utilization system. However, or dust explosions may occur, which could result heavy casualties significant economic losses. To prevent accidents system, present study takes of Shanxi Yiyang Energy Company as an example to identify composition hazardous factors Fault tree analysis (FTA) models were established with pipeline top events, importance each basic event was quantitatively analyzed using fuzzy fault (FFTA) method. results show that explosion are mostly caused by combination high-temperature ignition sources explosive materials. uneven ventilation carrying a large amount fundamental causes mining accidents. Consequently, based on general safety measures, indirect preheating, air methane removal, intelligent regulation proposed enhance

Language: Английский

Citations