Fault diagnosis of sensors in the control system of a steam turbine

Pawlak, M.; Buchta, Janusz; Oziemski, A.

doi:10.24136/jaeee.2019.004

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…To accurately simulate the dynamic characteristics of the DEH system, Liao [8] proposed a new feedback control system combined with an artificial neural network, which could effectively identify linear parameters and nonlinear parameters. Using fuzzy neural networks (fuzzy-NNs) to diagnose sensor faults in steam turbine DEH systems was proposed by Mariusz Pawlak et al [9]. Jin [10] established a steam turbine governor controller for the DEH system, changed the delay time of each component of the DEH system, and simulated the impact of delay failure on the DEH system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation

Zhong,

2023

PLoS ONE

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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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Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation

Zhong,

2023

PLoS ONE

View full text Add to dashboard Cite

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Fault diagnosis of sensors in the control system of a steam turbine

Cited by 1 publication

References 18 publications

Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation

Intelligent diagnosis and prediction of turbine digital electro-hydraulic control system faults: Design and experimentation

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