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Using Cased-Based Reasoning Domain Knowledge to Train a Back Propagation Neural Network in order to Classify Gear Faults in an Industrial Robot

Fulltext:


Authors:

Ella Olsson

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

21st International Congress and Exhibition. Condition Monitoring and Diagnostic Engineering Management. COMADEM

Publisher:

Czech Society for Non-Destructive Testing

Abstract

The classification performance of a back propagation neural network classifier highly depends on its training process. In this paper we use the domain knowledge stored in a Case-based reasoning system in order to train a back propagation neural network to classify gear faults in an industrial robot. Our approach is to compile domain knowledge from a Case-based reasoning system using attributes from previously stored cases. These attributes holds vital information usable in the training process. Our approach may be usable when a light-weight classifier is wanted due to e.g. lack of computing power or when only a part of the knowledge stored in the case base of a large Case-based reasoning system is needed. Further, no use of the usual sensor signal classification steps such as filtering and feature extraction are needed once the neural network classifier is successfully trained.

Bibtex

@inproceedings{Olsson1381,
author = {Ella Olsson},
title = {Using Cased-Based Reasoning Domain Knowledge to Train a Back Propagation Neural Network in order to Classify Gear Faults in an Industrial Robot},
editor = {Pavel Mazal, Raj B K N Rao, V{\'a}clav Svoboda},
month = {June},
year = {2008},
booktitle = {21st International Congress and Exhibition. Condition Monitoring and Diagnostic Engineering Management. COMADEM},
publisher = {Czech Society for Non-Destructive Testing},
url = {http://www.es.mdu.se/publications/1381-}
}