By Topic

Data-driven fault diagnosis of oil rig motor pumps applying automatic definition and selection of features

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

5 Author(s)
Wandekokem, E.D. ; Dept. of Comput. Sci., Fed. Univ. of Espirito Santo, Vitoria, Brazil ; de Aquino Franzosi, F.T. ; Rauber, T.W. ; Varejao, F.M.
more authors

We report about fault diagnosis experiments to improve the maintenance quality of motor pumps installed on oil rigs. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Features are extracted from the vibration signals to detect and diagnose misalignment and mechanical looseness problems. We show the results of automatic pattern recognition methods to define and select features that describe the faults of the provided examples. The support vector machine is chosen as the classification architecture.

Published in:

Diagnostics for Electric Machines, Power Electronics and Drives, 2009. SDEMPED 2009. IEEE International Symposium on

Date of Conference:

Aug. 31 20096-Sept. 3 2009