Title :
Real-Time Data Mining in Magnetic Flux Leakage Detecting in Boiler Pipeline
Author :
Ke MinYi ; Liao Pan ; Song XiaoChun
Author_Institution :
Sch. of Comput. Sci. & Technol., Hubei Univ. of Technol., Wuhan, China
Abstract :
For boiler in magnetic flux leakage testing data characteristics on the basis of full analysis, Combining the application of industrial control integrated automation needs, proposed the pipeline magnetic flux leakage testing data mining system framework. Through analysis of magnetic flux leakage pipeline inspection data and mines the key data. It could be better to achieve detection and prediction of the pipe flaw.
Keywords :
boilers; condition monitoring; data mining; magnetic flux; mechanical engineering computing; mechanical testing; pipelines; boiler pipeline; magnetic flux leakage detection; magnetic flux leakage test; pipe flaw detection; pipe flaw prediction; pipeline inspection; realtime data mining; Data mining; Magnetic flux leakage; Real-time; Time series;
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2010 International Conference on
Conference_Location :
ChangSha
Print_ISBN :
978-0-7695-4286-7
DOI :
10.1109/ICDMA.2010.243
