Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer Models DOI Creative Commons

Yunbin Ma,

Z. J. Shang, Jie Zheng

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2411 - 2411

Published: April 10, 2025

Traditional leakage prediction models for long-distance pipelines have limitations in effectively synchronizing spatial and temporal features of signals, leading to data processing that heavily relies on manual experience exhibits insufficient generalization capabilities. This paper introduces a novel detection localization algorithm oil gas pipelines, integrating wavelet denoising with Long Short-Term Memory (LSTM)-Transformer model. The proposed utilizes pressure sensors collect real-time pipeline applies eliminate noise from the signals. By combining LSTM’s feature extraction Transformer’s self-attention mechanism, we construct short-term average gradient-average instantaneous flow network model continuously predicts based gradient inputs, monitors deviations between actual predicted flow, employs curve distance accurately determine location. Experimental results Jilin-Changchun demonstrate possesses superior warning Specifically, accuracy reaches 99.995%, location error margin below 2.5%. Additionally, can detect leaks exceeding 0.6% main without generating false alarms during operation.

Language: Английский

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

1
Acoustic Identification of Water Supply Pipe Leakage Based on Bispectrum Analysis DOI
Zi‐Ming Feng,

Zhihong Long,

Liyun Peng

et al.

Journal of Pipeline Systems Engineering and Practice, Journal Year: 2025, Volume and Issue: 16(3)

Published: April 29, 2025

Language: Английский

Citations

0
Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer Models DOI Creative Commons

Yunbin Ma,

Z. J. Shang, Jie Zheng

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2411 - 2411

Published: April 10, 2025

Traditional leakage prediction models for long-distance pipelines have limitations in effectively synchronizing spatial and temporal features of signals, leading to data processing that heavily relies on manual experience exhibits insufficient generalization capabilities. This paper introduces a novel detection localization algorithm oil gas pipelines, integrating wavelet denoising with Long Short-Term Memory (LSTM)-Transformer model. The proposed utilizes pressure sensors collect real-time pipeline applies eliminate noise from the signals. By combining LSTM’s feature extraction Transformer’s self-attention mechanism, we construct short-term average gradient-average instantaneous flow network model continuously predicts based gradient inputs, monitors deviations between actual predicted flow, employs curve distance accurately determine location. Experimental results Jilin-Changchun demonstrate possesses superior warning Specifically, accuracy reaches 99.995%, location error margin below 2.5%. Additionally, can detect leaks exceeding 0.6% main without generating false alarms during operation.

Language: Английский

Citations

0