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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Veröffentlicht in:PloS one Jg. 18; H. 11; S. e0294413
Hauptverfasser: Zhong, Ling, Li, Qing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco Public Library of Science 15.11.2023
Public Library of Science (PLoS)
Schlagworte:
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
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung: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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ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0294413