By Topic

A novel competitive learning neural network based acoustic transmission system for oil-well monitoring

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)

The optimal operation of an oil well requires the periodic measurement of temperature and pressure at the downhole. In this paper, acoustic waves are used to transmit data to the surface through the pipeline column of the well, making up a wireless transmission system. Binary data is transmitted in two frequencies, using frequency-shift keying modulation. Such transmission faces problems with noise, attenuation, and, at pipeline joints, multiple reflections and nonlinear distortion. Hence, conventional demodulation techniques do not work well in this case. The neural network presented here classifies signals received by the receiver to estimate transmitted data, using a linear-vector-quantization-based network, with the help of a preprocessing procedure that transforms time-domain incoming signals in three-dimensional images. The results have been successfully verified. The neural network estimation principles presented in this paper can be easily applied to other patterns and time-domain recognition applications

Published in:

Industry Applications, IEEE Transactions on  (Volume:36 ,  Issue: 2 )