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http://dx.doi.org/10.7842/kigas.2014.18.3.53

A Study on Fault Detection Monitoring and Diagnosis System of CNG Stations based on Principal Component Analysis(PCA)  

Lee, Kijun (Korea Institute of Fire Industry & Technology)
Lee, Bong Woo (Korea Institute of Fire Industry & Technology)
Choi, Dong-Hwang (Korea Institute of Fire Industry & Technology)
Kim, Tae-Ok (Department of Chemical Engineering, Myongji University)
Shin, Dongil (Department of Chemical Engineering, Myongji University)
Publication Information
Journal of the Korean Institute of Gas / v.18, no.3, 2014 , pp. 53-59 More about this Journal
Abstract
In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.
Keywords
fault detection; monitoring; diagnosis; principal component analysis (PCA); CNG station;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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