Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger

Heat exchangers play essential roles in the oil and gas production process for convective heat transfer and heat conduction. The health management of heat exchangers stays in the direct monitoring of performance parameters. Aiming at the difficulty of precise fault identification and quantification...

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Main Authors: Xiaogang Qin, Shiwei Yan, Haibo Xu, Yi Gao, Yanbing Yu, Jinjiang Wang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/17/23/6113
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author Xiaogang Qin
Shiwei Yan
Haibo Xu
Yi Gao
Yanbing Yu
Jinjiang Wang
author_facet Xiaogang Qin
Shiwei Yan
Haibo Xu
Yi Gao
Yanbing Yu
Jinjiang Wang
author_sort Xiaogang Qin
collection DOAJ
description Heat exchangers play essential roles in the oil and gas production process for convective heat transfer and heat conduction. The health management of heat exchangers stays in the direct monitoring of performance parameters. Aiming at the difficulty of precise fault identification and quantification for heat exchangers in multiple unknown failure modes, a data-model fusion-driven fault quantitative diagnosis method is proposed. Firstly, based on the monitoring data such as temperature, pressure and flow rate, the secondary parameters characterizing the heat exchanger running state are constructed combined with structural physical parameters. Then, by analyzing the correlation among parameter variation, failure modes and deterioration degree, a qualitative inference model of heat exchanger is formed for fault identification, where weights of parameters are introduced based on their sensitivity for different failure modes. After the fault mode is identified, to achieve quantitative analysis of the failure degree, an index-integrated mechanism equation is constructed using monitoring data and secondary parameters, where the index is dynamically modified by online data. Finally, a heat exchanger experiment is carried out to demonstrate the robustness and accuracy of the proposed method.
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issn 1996-1073
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publisher MDPI AG
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series Energies
spelling doaj-art-76eaf8a6757546eabb9778c2f07c4ccc2025-08-20T02:50:33ZengMDPI AGEnergies1996-10732024-12-011723611310.3390/en17236113Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat ExchangerXiaogang Qin0Shiwei Yan1Haibo Xu2Yi Gao3Yanbing Yu4Jinjiang Wang5CNOOC China Limited Beijing Research Center, Beijing 100028, ChinaSchool of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, ChinaCNOOC China Limited Beijing Research Center, Beijing 100028, ChinaSchool of Safety and Ocean Engineering, China University of Petroleum, Beijing 102249, ChinaExploration and Development Department of CNOOC Limited, Beijing 100010, ChinaKey Laboratory of Oil and Gas Production Equipment Quality Inspection and Health Diagnosis, State Administration for Market Regulation, Beijing 100088, ChinaHeat exchangers play essential roles in the oil and gas production process for convective heat transfer and heat conduction. The health management of heat exchangers stays in the direct monitoring of performance parameters. Aiming at the difficulty of precise fault identification and quantification for heat exchangers in multiple unknown failure modes, a data-model fusion-driven fault quantitative diagnosis method is proposed. Firstly, based on the monitoring data such as temperature, pressure and flow rate, the secondary parameters characterizing the heat exchanger running state are constructed combined with structural physical parameters. Then, by analyzing the correlation among parameter variation, failure modes and deterioration degree, a qualitative inference model of heat exchanger is formed for fault identification, where weights of parameters are introduced based on their sensitivity for different failure modes. After the fault mode is identified, to achieve quantitative analysis of the failure degree, an index-integrated mechanism equation is constructed using monitoring data and secondary parameters, where the index is dynamically modified by online data. Finally, a heat exchanger experiment is carried out to demonstrate the robustness and accuracy of the proposed method.https://www.mdpi.com/1996-1073/17/23/6113heat exchangerfault identificationfault quantitation indexdata-model hybrid
spellingShingle Xiaogang Qin
Shiwei Yan
Haibo Xu
Yi Gao
Yanbing Yu
Jinjiang Wang
Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
Energies
heat exchanger
fault identification
fault quantitation index
data-model hybrid
title Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
title_full Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
title_fullStr Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
title_full_unstemmed Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
title_short Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger
title_sort data model fusion driven method for fault quantitative diagnosis of heat exchanger
topic heat exchanger
fault identification
fault quantitation index
data-model hybrid
url https://www.mdpi.com/1996-1073/17/23/6113
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