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Case-Based Reasoning Supports Fault Diagnosis Using Sensor Information

Authors:

Ning Xiong, Peter Funk, Tomas Olsson

Research groups:

Artificial Intelligence and Intelligent Systems
Learning and Optimisation

Publication Type:

Conference/Workshop Paper

Venue:

The 2nd International Workshop and Congress on eMaintenance

Abstract

Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

Bibtex

@inproceedings{Xiong2693,
author = {Ning Xiong and Peter Funk and Tomas Olsson},
title = {Case-Based Reasoning Supports Fault Diagnosis Using Sensor Information},
editor = { Uday Kumar, Ramin Karim, Aditya Parida, . Phillip Tretten},
month = {November},
year = {2012},
booktitle = {The 2nd International Workshop and Congress on eMaintenance},
url = {http://www.es.mdu.se/publications/2693-}
}