Relationship Prediction Based on Graph Model for Steam Turbine Control Valve

The control valve is an important piece of equipment in the steam turbine, which frequently suffers from the fault of the dead zone. The graph model is a promising method for dead zone detection, yet establishing an accurate and completed graph topology is not an easy task due to limited mechanism k...

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Main Authors: Yi-Jing Zhang, Li-Sheng Hu
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Actuators
Subjects:
Online Access:https://www.mdpi.com/2076-0825/10/5/91
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author Yi-Jing Zhang
Li-Sheng Hu
author_facet Yi-Jing Zhang
Li-Sheng Hu
author_sort Yi-Jing Zhang
collection DOAJ
description The control valve is an important piece of equipment in the steam turbine, which frequently suffers from the fault of the dead zone. The graph model is a promising method for dead zone detection, yet establishing an accurate and completed graph topology is not an easy task due to limited mechanism knowledge. Hence, a graph model is proposed to predict the links in the graph and estimate the relationship between variables of related equipment of the control valve. The graph convolution is conducted on the uncompleted graph to learn the low-level representations of the graph nodes, and the score function is used to evaluate the probability of the existence of links between a pair of graph nodes. Results demonstrate a test accuracy of 99.2% for the link prediction, and follow the principles of thermodynamics in the steam turbine. Consequently, the proposed graph model is capable of estimating the relationships for the steam turbine control valve, and other inter-connected industrial systems.
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spelling doaj.art-6dc580aee9d2432d9fd203e8a01916b82023-11-21T17:18:01ZengMDPI AGActuators2076-08252021-04-011059110.3390/act10050091Relationship Prediction Based on Graph Model for Steam Turbine Control ValveYi-Jing Zhang0Li-Sheng Hu1Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, ChinaDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, ChinaThe control valve is an important piece of equipment in the steam turbine, which frequently suffers from the fault of the dead zone. The graph model is a promising method for dead zone detection, yet establishing an accurate and completed graph topology is not an easy task due to limited mechanism knowledge. Hence, a graph model is proposed to predict the links in the graph and estimate the relationship between variables of related equipment of the control valve. The graph convolution is conducted on the uncompleted graph to learn the low-level representations of the graph nodes, and the score function is used to evaluate the probability of the existence of links between a pair of graph nodes. Results demonstrate a test accuracy of 99.2% for the link prediction, and follow the principles of thermodynamics in the steam turbine. Consequently, the proposed graph model is capable of estimating the relationships for the steam turbine control valve, and other inter-connected industrial systems.https://www.mdpi.com/2076-0825/10/5/91graph convolutionlink predictionrelationship predictionscore functionsteam turbine control valve
spellingShingle Yi-Jing Zhang
Li-Sheng Hu
Relationship Prediction Based on Graph Model for Steam Turbine Control Valve
Actuators
graph convolution
link prediction
relationship prediction
score function
steam turbine control valve
title Relationship Prediction Based on Graph Model for Steam Turbine Control Valve
title_full Relationship Prediction Based on Graph Model for Steam Turbine Control Valve
title_fullStr Relationship Prediction Based on Graph Model for Steam Turbine Control Valve
title_full_unstemmed Relationship Prediction Based on Graph Model for Steam Turbine Control Valve
title_short Relationship Prediction Based on Graph Model for Steam Turbine Control Valve
title_sort relationship prediction based on graph model for steam turbine control valve
topic graph convolution
link prediction
relationship prediction
score function
steam turbine control valve
url https://www.mdpi.com/2076-0825/10/5/91
work_keys_str_mv AT yijingzhang relationshippredictionbasedongraphmodelforsteamturbinecontrolvalve
AT lishenghu relationshippredictionbasedongraphmodelforsteamturbinecontrolvalve