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Fault Diagnosis in Industry Using Sensor Readings and Case-Based Reasoning
Publication Type:
Journal article
Venue:
Journal of Intelligent & Fuzzy Systems
Abstract
Abstract. Fault diagnosis of industrial equipments becomes increasingly important
for improving the quality of manufacturing and reducing the cost for
product testing. Developing a fast and reliable diagnosis system presents a
challenge issue in many complex industrial scenarios. The major difficulties
therein arise from contaminated sensor readings caused by heavy background
noise as well as the unavailability of experienced technicians for support. In
this paper we propose a novel method for diagnosis of faults by means of
case-based reasoning and signal processing. The received sensor signals are
processed by wavelet analysis to filter out noise and at the same time to extract
a group of related features that constitutes a reduced representation of
the original signal. The derived feature vector is then forwarded to a classification
component that uses case-based reasoning to recommend a fault class
for the probe case. This recommendation is based on previously classified
cases in a case library. Case-based diagnosis has attractive properties in that it
enables reuse of past experiences whereas imposes no demand on the size of
the case base. The proposed approach has been applied to fault diagnosis of
industrial robots at ABB Robotics and the results of experiments are very
promising.
Bibtex
@article{Olsson601,
author = {Ella Olsson and Peter Funk and Ning Xiong},
title = {Fault Diagnosis in Industry Using Sensor Readings and Case-Based Reasoning},
volume = {Vol. 15, ISSN 1064-1},
month = {December},
year = {2004},
journal = {Journal of Intelligent {\&} Fuzzy Systems},
publisher = {IOS Press},
url = {http://www.es.mdu.se/publications/601-}
}