Abstract—Present study addresses the monitoring of drum boiler process. Methodologies; based on clustering time series data and moving window based pattern matching have been proposed for the detection of fault in the chosen process. Design databases were created for the process by simulating the developed process model. A modified k-means clustering algorithm using similarity measure as a convergence criterion has been adopted for discriminating among time series data pertaining to various operating conditions. The proposed distance and PCA based combined similarity along with the moving window approach were used to discriminate among the normal operating conditions as well as detection of faults for the processes taken up.
Index Terms—Drum boiler, k-means clustering, Movingwindow, PCA, Pattern matching.
Seshu K. Damarla, M.Tech student: E-mail: Seshu.chemical@gmail.com
Madhusree Kundu, Associate Professor, Department of Chemical Engineering, National Institute of Technology, Rourkela, India.E-mail:mkundu@nitrkl.ac.in, madhushreek@yahoo.com, Fax:0661-2462999
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Cite: Seshu K. Damarla and Madhusree Kundu, "Monitoring of Drum-boiler Process Using Statistical Techniques," International Journal of Chemical Engineering and Applications vol. 2, no. 3, pp. 173-180, 2011.
