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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/1490
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Full metadata record
| DC Field | Value | Language |
|---|
| contributor.author | Damarla, S K | - |
| contributor.author | Kundu, M | - |
| date.accessioned | 2011-07-12T06:20:46Z | - |
| date.available | 2011-07-12T06:20:46Z | - |
| date.issued | 2011-06 | - |
| identifier.citation | International Journal of Chemical Engineering and Applications, Vol. 2 , No. 3 , June 2011 | en |
| identifier.uri | http://hdl.handle.net/2080/1490 | - |
| description | Copyright belongs to International Journal of Chemical Engineering and Applications (IJCEA) | en |
| description.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. | en |
| format.extent | 748905 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | International Journal of Chemical Engineering and Applications (IJCEA) | en |
| subject | Drum boiler | en |
| subject | k-means clustering | en |
| subject | Moving window | en |
| subject | PCA | en |
| subject | Pattern matching | en |
| title | Monitoring of Drum-boiler Process Using Statistical Techniques | en |
| type | Article | en |
| Appears in Collections: | Journal Articles
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Files in This Item:
| File |
Description |
Size | Format |
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| Monitoring of DrumBoiler.pdf | | 731Kb | Adobe PDF | View/Open |
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