Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation
A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK tool. This research combined the advantages of the gray system and neural network to build a multi-parameter gray error neural network fault pre...
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| Published in: | PloS one Vol. 18; no. 11; p. e0294413 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Public Library of Science
15.11.2023
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK tool. This research combined the advantages of the gray system and neural network to build a multi-parameter gray error neural network fault prediction model for the first time. Furthermore, an embedded platform for intelligent fault diagnosis and prediction was developed using an Application Specific Integrated Circuit chip. The results show that the simulation model of the DEH system has good performance. A jam fault, internal leakage, and a device fault could be accurately identified through the fault diagnosis model. The multi-parameter gray error neural network prediction model improves the accuracy of fault prediction. The embedded platform developed by the Application Specific Integrated Circuit chip solves the problem of transmission limitation and insufficient computing power. It realizes the intelligent diagnosis and prediction of DEH system faults and guarantees the regular operation of the DEH system. |
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| AbstractList | A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK tool. This research combined the advantages of the gray system and neural network to build a multi-parameter gray error neural network fault prediction model for the first time. Furthermore, an embedded platform for intelligent fault diagnosis and prediction was developed using an Application Specific Integrated Circuit chip. The results show that the simulation model of the DEH system has good performance. A jam fault, internal leakage, and a device fault could be accurately identified through the fault diagnosis model. The multi-parameter gray error neural network prediction model improves the accuracy of fault prediction. The embedded platform developed by the Application Specific Integrated Circuit chip solves the problem of transmission limitation and insufficient computing power. It realizes the intelligent diagnosis and prediction of DEH system faults and guarantees the regular operation of the DEH system. A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK tool. This research combined the advantages of the gray system and neural network to build a multi-parameter gray error neural network fault prediction model for the first time. Furthermore, an embedded platform for intelligent fault diagnosis and prediction was developed using an Application Specific Integrated Circuit chip. The results show that the simulation model of the DEH system has good performance. A jam fault, internal leakage, and a device fault could be accurately identified through the fault diagnosis model. The multi-parameter gray error neural network prediction model improves the accuracy of fault prediction. The embedded platform developed by the Application Specific Integrated Circuit chip solves the problem of transmission limitation and insufficient computing power. It realizes the intelligent diagnosis and prediction of DEH system faults and guarantees the regular operation of the DEH system.A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK tool. This research combined the advantages of the gray system and neural network to build a multi-parameter gray error neural network fault prediction model for the first time. Furthermore, an embedded platform for intelligent fault diagnosis and prediction was developed using an Application Specific Integrated Circuit chip. The results show that the simulation model of the DEH system has good performance. A jam fault, internal leakage, and a device fault could be accurately identified through the fault diagnosis model. The multi-parameter gray error neural network prediction model improves the accuracy of fault prediction. The embedded platform developed by the Application Specific Integrated Circuit chip solves the problem of transmission limitation and insufficient computing power. It realizes the intelligent diagnosis and prediction of DEH system faults and guarantees the regular operation of the DEH system. |
| Audience | Academic |
| Author | Li, Qing Zhong, Ling |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0321940 |
| Cites_doi | 10.1016/j.asoc.2020.106996 10.1016/j.energy.2014.01.042 10.1016/j.ymssp.2008.01.004 10.1109/ICMTMA.2009.79 10.1016/j.tafmec.2023.103782 10.18186/thermal.1187839 10.1016/j.measurement.2019.01.022 10.1109/ACCESS.2020.3012091 10.1016/j.tafmec.2023.103881 10.1109/CISP.2013.6745227 10.4028/www.scientific.net/AMM.69.51 10.1590/S1516-89132006000200011 10.1177/0954408920973117 10.1109/AUS.2016.7748035 10.1109/ICAL.2008.4636252 10.3390/a16020098 10.24136/jaeee.2019.004 |
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| Copyright | COPYRIGHT 2023 Public Library of Science 2023 Zhong, Li. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright: © 2023 Zhong, Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2023 Zhong, Li. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1371/journal.pone.0294413 |
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| Snippet | A physical modeling approach was adopted to build a Digital Electro-Hydraulic Control (DEH) system simulation model and the fault models using the SIMULINK... |
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| SubjectTerms | Analysis Application specific integrated circuits Control systems Control systems design Custom integrated circuits Failure Fault diagnosis Hydraulic control Hydraulics Medical diagnosis Methods Neural networks Parameter estimation Parameter identification Power plants Prediction models Simulation Simulation methods Simulation models Turbines |
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| Title | Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation |
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