Publications:Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor

From ISLAB/CAISR

Do not edit this section

Keep all hand-made modifications below

Title Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor
Author Magnus Svensson and Magnus Forsberg and Stefan Byttner and Thorsteinn Rögnvaldsson
Year 2009
PublicationType Conference Paper
Journal
HostPublication Proceeding Intelligent Systems and Control (ISC 2009)
Conference Intelligent Systems and Control 2009, Cambridge, Mass., USA, Nov. 2-4, 2009
DOI
Diva url http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:327075
Abstract A new approach to improve fault detection is proposed. The method takes benefit of using a population of systems to dynamically define a norm of how the system works. The norm is derived from self-organizing algorithms which generate a low dimensional representation of how selected feature data are correlated. The feature data is selected from the state variables and from the control signals. The self-organizing method and limited number of feature signals enable fast deviation detection and low computational footprint on each system to be analyzed. The comparison analysis between the systems is performed at a service centre, to where the low-dimensional representations of the systems are transmitted. The method is demonstrated on a simulated DC-motor and the results are promising for deviation detection.