Real-time quality control of pipes using neural network prediction error signals for defect detection in time area

1999 ◽  
Author(s):  
Alexander M. Akhmetshin ◽  
Andrey P. Gvozdak
2012 ◽  
Vol 524-527 ◽  
pp. 1327-1330 ◽  
Author(s):  
Ying Ming Zhou ◽  
Shu Wei Wang ◽  
Lin Lin

With the constant expansion of super heavy oil SAGD conversion development, the accurate testing of the crude oil in the high moisture content range is particularly important. In this paper, against the characteristics of Adopting SAGD technology exploiting heavy oil, BP neural network prediction model and calculation method has been adopted to predict the moisture content of crude oil. Through the study, the experimental data of the model were verified by the maximum prediction error is less than 3%, the accuracy of the forecast moisture content of crude oil to meet the site requirements. Through this study, the experimental data to the model was validated by the maximum prediction error is less than3%, the prediction accuracy of which to moisture content of crude oil is able to meet the requirements of the project site.


2019 ◽  
Vol 2 ◽  
pp. 100011 ◽  
Author(s):  
Dario Pavllo ◽  
Mathias Delahaye ◽  
Thibault Porssut ◽  
Bruno Herbelin ◽  
Ronan Boulic

Author(s):  
Naohisa NISHIDA ◽  
Tatsumi OBA ◽  
Yuji UNAGAMI ◽  
Jason PAUL CRUZ ◽  
Naoto YANAI ◽  
...  

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