Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer Models Article Swipe
Yunbin Ma
,
Z. J. Shang
,
Jie Zheng
,
Yichen Zhang
,
Guangyuan Weng
,
Shu Zhao
,
Cheng Bi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25082411
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25082411
Traditional leakage prediction models for long-distance pipelines have limitations in effectively synchronizing spatial and temporal features of leakage signals, leading to data processing that heavily relies on manual experience and exhibits insufficient generalization capabilities. This paper introduces a novel leakage detection and localization algorithm for oil and gas pipelines, integrating wavelet denoising with a Long Short-Term Memory (LSTM)-Transformer model. The proposed algorithm utilizes pressure sensors to collect real-time pipeline pressure data and applies wavelet denoising to eliminate noise from the pressure signals. By combining LSTM’s temporal feature extraction with the Transformer’s self-attention mechanism, we construct a short-term average pressure gradient-average instantaneous flow network model. This model continuously predicts pipeline flow based on real-time pressure gradient inputs, monitors deviations between actual and predicted flow, and employs a pressure curve distance algorithm to accurately determine the leakage location. Experimental results from the Jilin-Changchun long-distance oil pipeline demonstrate that the model possesses superior leakage warning and localization capabilities. Specifically, the leakage prediction accuracy reaches 99.995%, with a leakage location error margin below 2.5%. Additionally, the model can detect leaks exceeding 0.6% of the main pipeline flow without generating false alarms during operation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25082411
- OA Status
- gold
- Cited By
- 4
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409328883
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409328883Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25082411Digital Object Identifier
- Title
-
Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-10Full publication date if available
- Authors
-
Yunbin Ma, Z. J. Shang, Jie Zheng, Yichen Zhang, Guangyuan Weng, Shu Zhao, Cheng BiList of authors in order
- Landing page
-
https://doi.org/10.3390/s25082411Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/s25082411Direct OA link when available
- Concepts
-
Pipeline transport, Wavelet, Computer science, Leakage (economics), Noise reduction, Artificial intelligence, Algorithm, Pattern recognition (psychology), Real-time computing, Engineering, Environmental engineering, Macroeconomics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| display_name | Research on Leak Detection and Localization Algorithm for Oil and Gas Pipelines Using Wavelet Denoising Integrated with Long Short-Term Memory (LSTM)–Transformer Models |
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| updated_date | 2025-11-06T03:46:38.306776 |
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