Cited by Drilling Bit Wear Monitoring Method Based on Multi-Head Attention Mechanism and Hybrid Neural Networks

Application of hybrid machine learning algorithm in multi-objective optimization of green building energy efficiency DOI

Yi Zhu, Wen Xu, Wenhong Luo

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 133581 - 133581

Published: Jan. 1, 2025

Language: Английский

Citations

An Integrated Deep Learning Model for Intelligent Recognition of Long-distance Natural Gas Pipeline Features DOI

Lin Wang,

Wannian Guo,

Junyu Guo

et al.

Reliability Engineering & System Safety, Journal Year: 2024, Volume and Issue: unknown, P. 110664 - 110664

Published: Nov. 1, 2024

Language: Английский

Citations

A hybrid fault diagnosis scheme for milling tools using MWN-CBAM-PatchTST network with acoustic emission signals DOI

Junyu Guo, Hongyun Luo, Yongming Xing

et al.

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 29

Published: Jan. 8, 2025

Milling tools are critical to machining and manufacturing processes. Accurate diagnosis identification of faults occurring in milling during their operation utmost importance for maintaining the reliability availability these tools, minimise machine downtime overall costs. This paper presents a fault network model based on acoustic emission signals. The integrates multilayer wavelet CNN (MWN) consisting discrete transform (DWT) convolutional neural (CNN), block attention module (CBAM), PatchTST module. MWN uses transformation withdraw multi-scale features from signals, thus improving sensitivity small variations emission. CBAM improves feature representation by focusing channels regions, while self-attention mechanism optimise processing long-range dependencies. synergy mechanisms results superior performance, outperforming traditional diagnostic methods. Bayesian optimisation is used select hyperparameters, eliminating subjective bias associated with manual range setting. Validation experiments using dataset, including ablation studies comparative tests, demonstrated that achieves an accuracy over 98%, validating its generalisation capability effectiveness diagnosing tool

Language: Английский

Citations

A hybrid deep learning model towards flow pattern identification of gas-liquid two-phase flows in horizontal pipe DOI

Tingxia Ma,

Tianxin Wang, Lin Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135141 - 135141

Published: Feb. 1, 2025

Language: Английский

Citations

Deep Learning-Based Fatigue Strength Prediction for Ferrous Alloy DOI

Zhikun Huang,

Jingchao Yan,

Jianlong Zhang

et al.

Processes, Journal Year: 2024, Volume and Issue: 12(10), P. 2214 - 2214

Published: Oct. 11, 2024

As industrial development drives the increasing demand for steel, accurate estimation of material’s fatigue strength has become crucial. Fatigue strength, a critical mechanical property is primary factor in component failure within engineering applications. Traditional testing both costly and time-consuming, can lead to severe consequences. Therefore, need develop faster more efficient methods predicting evident. In this paper, dataset was established, incorporating data on material element composition, physical properties, performance parameters that influence strength. A machine learning regression model then applied facilitate rapid prediction ferrous alloys. Twenty characteristic parameters, selected their practical relevance applications, were used as input variables, with output. Multiple algorithms trained dataset, deep employed The models evaluated using metrics such MAE, RMSE, R2, MAPE. results demonstrated superiority proposed effectiveness methodologies.

Language: Английский

Citations

Improving active power regulation for wind turbine by phase leading cascaded error-based active disturbance rejection control and multi-objective optimization DOI

Xuehan Li, Wei Wang, Fang Fang

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: 243, P. 122629 - 122629

Published: Feb. 7, 2025

Language: Английский

Citations

Multi-label learning for fault diagnosis of pumping units with one positive label DOI

Kun Qian, Jinyu Tang, Qiulin Zhao

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113014 - 113014

Published: March 1, 2025

Language: Английский

Citations

Enhancing the safety of hydroelectric power generation systems: an intelligent identification of axis orbits based on a nonlinear dynamics method DOI

Fei Chen, Jie Liu, Xiaoxi Hu

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135864 - 135864

Published: April 1, 2025

Language: Английский

Citations

Advancement of artificial intelligence applications in hydrocarbon well drilling technology: A review DOI

Shadfar Davoodi, Mohammed Al-Shargabi, David A. Wood

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113129 - 113129

Published: April 1, 2025

Language: Английский

Citations

Fault Diagnosis Method of Electro-hydrostatic Actuator Based on Transfer Learning DOI

Jiaqi Lv, Wei Zhong,

Jianfei Zhu

et al.

Journal of Physics Conference Series, Journal Year: 2025, Volume and Issue: 2999(1), P. 012032 - 012032

Published: April 1, 2025

Abstract Since the actual fault samples of electro-hydrostatic actuators are difficult to obtain and number is small, an effective sufficient simulation data set obtained by constructing a model actuator, method migration component analysis used reduce difference distribution. The feature knowledge learned from through deep network migrated solve problem classification under condition small scarce data. experimental results show that algorithm can construct based on many sample data, transfer learning method, task significantly reduced, diagnosis accuracy robustness improved. It effectively in case sparse unbalanced

Language: Английский

Citations