Pipeline and Rotating Pump Condition Monitoring Based on Sound Vibration Feature-Level Fusion DOI Creative Commons

Yu Wan,

Shaochen Lin, Yan Gao

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(12), P. 921 - 921

Published: Dec. 16, 2024

The rotating pump of pipelines are susceptible to damage based on extended operations in a complex environment high temperature and pressure, which leads abnormal vibrations noises. Currently, the method for detecting conditions pumps primarily involves identifying their sounds vibrations. Due background noise, performance condition monitoring is unsatisfactory. To overcome this issue, pipeline proposed by extracting fusing sound vibration features different ways. Firstly, hand-crafted feature set established from two aspects vibration. Moreover, convolutional neural network (CNN)-derived one-dimensional CNN (1D CNN). For CNN-derived sets, selection presented significant ranking according importance, calculated ReliefF random forest score. Finally, applied at level. According signals obtained experimental platform, was evaluated, showing an average accuracy 93.27% conditions. effectiveness superiority manifested through comparison ablation experiments.

Language: Английский

Experimental study on leakage monitoring of buried water pipelines based on actively heated optical frequency domain reflection technology DOI
Lin Cheng, Yuheng Zhang,

Z.J. Wang

et al.

International Journal of Thermal Sciences, Journal Year: 2025, Volume and Issue: 211, P. 109685 - 109685

Published: Jan. 10, 2025

Language: Английский

Citations

0
Zero-shot pipeline fault detection using percussion method and multi-attribute learning model DOI

Longguang Peng,

Wenjie Huang,

Jicheng Zhang

et al.

Mechanical Systems and Signal Processing, Journal Year: 2025, Volume and Issue: 228, P. 112427 - 112427

Published: Feb. 6, 2025

Language: Английский

Citations

0
State prediction for multiple diffusion targets based on point pattern physics-informed neural network DOI
Qiankun Sun, Lei Cai, Xiaochen Qin

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129714 - 129714

Published: Feb. 1, 2025

Language: Английский

Citations

0
Leak detection in water supply networks using two-stage temporal segmentation and incremental learning for non-stationary acoustic signals DOI Creative Commons

Xingke Ma,

Yipeng Wu, Guancheng Guo

et al.

Water Research X, Journal Year: 2025, Volume and Issue: unknown, P. 100333 - 100333

Published: March 1, 2025

Language: Английский

Citations

0
An efficient intelligent detection method for water pipeline leakages utilizing homologous Multi-Modal signal fusion DOI

Yijie Zhou,

Huizhou Liu, Xiaochun Cao

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117562 - 117562

Published: April 1, 2025

Language: Английский

Citations

0
Multi-objective evolutionary co-learning framework for energy-efficient hybrid flow-shop scheduling problem with human-machine collaboration DOI
Jiawei Wu, Yong Liu, Yani Zhang

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 95, P. 101932 - 101932

Published: April 14, 2025

Language: Английский

Citations

0
Pipeline and Rotating Pump Condition Monitoring Based on Sound Vibration Feature-Level Fusion DOI Creative Commons

Yu Wan,

Shaochen Lin, Yan Gao

et al.

Machines, Journal Year: 2024, Volume and Issue: 12(12), P. 921 - 921

Published: Dec. 16, 2024

The rotating pump of pipelines are susceptible to damage based on extended operations in a complex environment high temperature and pressure, which leads abnormal vibrations noises. Currently, the method for detecting conditions pumps primarily involves identifying their sounds vibrations. Due background noise, performance condition monitoring is unsatisfactory. To overcome this issue, pipeline proposed by extracting fusing sound vibration features different ways. Firstly, hand-crafted feature set established from two aspects vibration. Moreover, convolutional neural network (CNN)-derived one-dimensional CNN (1D CNN). For CNN-derived sets, selection presented significant ranking according importance, calculated ReliefF random forest score. Finally, applied at level. According signals obtained experimental platform, was evaluated, showing an average accuracy 93.27% conditions. effectiveness superiority manifested through comparison ablation experiments.

Language: Английский

Citations

1