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Classification and key feature extraction for equipment health monitoring

Presentation held at 27th Advanced Process Control Conference, APC 2015, Austin, USA
 
: Krauel, Christopher; Weishäupl, Laura; Petzold, Lisa; Pfeffer, Markus; Bauer, Anton

:
presentation urn:nbn:de:0011-n-3936329 (641 KByte PDF)
MD5 Fingerprint: 1ec64b1f585f85ddfd69ae599bcd783e
Created on: 19.5.2016


2015, 24 Folien
Advanced Process Control Conference (APC) <27, 2015, Austin/Tex.>
English
Presentation, Electronic Publication
Fraunhofer IISB ()
classification; key feature extraction; Equipment Health Factor (EHF); condition monitoring

Abstract
This presentation deals with the implementation of an autonomous variable classification and key feature extraction methodology for equipment health factor monitoring. Our approach concentrates on the autonomous classification of different variable types which are defined by different variable behaviors and the investigation of specified statistical feature extraction methods for every respective class. The goal is to provide a generic method that chooses the correct feature extraction algorithm depending on the nature of the input variables.

: http://publica.fraunhofer.de/documents/N-393632.html