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Monitoring data early warning method of natural gas compressor unit based on VCW-Informer
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Junming YAO1, 2, Wei LIANG**, 1, 2, Zhiming ZHENG3, Tianchang HUANG1, 2, Qianjun FU1, 2, Chunyan LIAO1, 2
China Safety Science Journal | 2025, 35(7) : 167 - 175
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China Safety Science Journal | 2025, 35(7): 167-175
Safety engineering technology
Monitoring data early warning method of natural gas compressor unit based on VCW-Informer
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Junming YAO1, 2, Wei LIANG**, 1, 2, Zhiming ZHENG3, Tianchang HUANG1, 2, Qianjun FU1, 2, Chunyan LIAO1, 2
Affiliations
  • 1College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
  • 2Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
  • 3Sino-Pipeline International Company Limited (SPI), Beijing 102249, China
Published: 2025-07-28 doi: 10.16265/j.cnki.issn1003-3033.2025.07.0604
Outline
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In order to further enhance the early abnormal warning capability of natural gas compressor units, a novel method was proposed based on the VMD (Variational Mode Decomposition) algorithm, Informer algorithm, 3σ criterion, and Correlation-Weight optimization. A predictive model was constructed using the Informer architecture, in which the monitoring data were decomposed by VMD algorithm into multi-scale features of different frequencies to serve as model inputs. During the training process, the weight coefficients between each decomposed component and the original signal were calculated to optimize and adjust the internal model parameters. Furthermore, the prediction reconstruction results were combined with the 3σ statistical criterion to further improve the warning performance. Two segments of normal and abnormal pressure differential monitoring data from field compressor units were collected for experimental validation. The results show that, compared with other prediction methods, the proposed warning method achieves the lowest prediction errors. For the prediction of normal box pressure differentials, the errors are reduced by 66.67%-71.43% (Mean Squared Error, MSE), 36.67%-45.45% (Mean Absolute Error, MAE), 40.17%-45.42% (Root Mean Squared Error, RMSE), and 36.57%-45.72% (Mean Absolute Percentage Error, MAPE). For the abnormal inlet pressure differentials, the errors are reduced by 64.43%-71.12% (MSE), 44.02%-52.27% (MAE), 40.36%-45.53% (RMSE), and 37.24%-47.79% (MAPE). The proposed method exhibits superior prediction accuracy in both detailed and trend features. In the test set, it provides anomaly warning 60 minutes in advance, thereby improving the reliability of safe and stable operation of the compressor units.

variational correlation-weight Informer (VCW-Informer)  /  natural gas compressor  /  monitoring data  /  abnormal early warning  /  deep learning  /  signal decomposition
Junming YAO, Wei LIANG, Zhiming ZHENG, Tianchang HUANG, Qianjun FU, Chunyan LIAO. Monitoring data early warning method of natural gas compressor unit based on VCW-Informer[J]. China Safety Science Journal, 2025 , 35 (7) : 167 -175 . DOI: 10.16265/j.cnki.issn1003-3033.2025.07.0604
Year 2025 volume 35 Issue 7
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.07.0604
  • Receive Date:2025-03-14
  • Online Date:2026-07-09
  • Published:2025-07-28
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  • Received:2025-03-14
  • Revised:2025-05-18
Funding
Affiliations
    1College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
    2Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
    3Sino-Pipeline International Company Limited (SPI), Beijing 102249, China
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表12种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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