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http://dx.doi.org/10.3795/KSME-B.2015.39.2.199

A Monitoring System Based on an Artificial Neural Network for Real-Time Diagnosis on Operating Status of Piping System  

Jeon, Min Gyu (Gas Solution Center, Korea Maritime Univ.)
Cho, Gyong Rae (Division of Mechanical and Information Engineering, Korea Maritime Univ.)
Lee, Kang Ki (Department of Offshore Plant Managements, Korea Maritime Univ.)
Doh, Deog Hee (Division of Mechanical and Information Engineering, Korea Maritime Univ.)
Publication Information
Transactions of the Korean Society of Mechanical Engineers B / v.39, no.2, 2015 , pp. 199-206 More about this Journal
Abstract
In this study, a new diagnosis method which can predict the working states of a pipe or its element in realtime is proposed by using an artificial neural network. The displacement data of an inspection element of a piping system are obtained by the use of PIV (particle image velocimetry), and are used for teaching a neural network. The measurement system consists of a camera, a light source and a host computer in which the artificial neural network is installed. In order to validate the constructed monitoring system, performance test was attempted for two kinds of mobile phone of which vibration modes are known. Three values of acceleration (minimum, maximum, mean) were tested for teaching the neural network. It was verified that mean values were appropriate to be used for monitoring data. The constructed diagnosis system could monitor the operation condition of a gas pipe.
Keywords
Piping System; Vibration; Camera Image; Displacements; PIV; Artificial Neural Network; Real-Time Learning; Real-Time Monitoring; Mobile Phone; Gas Pipe;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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