|
Digital Library of the
European Council for Modelling and Simulation |
Title: |
Fault Diagnosis of Complex Systems Based on Modular Knowledge
Base and Information Compression |
Authors: |
Gancho Vachkov |
Published in: |
(2006).ECMS
2006 Proceedings edited by: W. Borutzky, A. Orsoni, R. Zobel. European
Council for Modeling and Simulation. doi:10.7148/2006 ISBN:
0-9553018-0-7 20th
European Conference on Modelling and Simulation, Bonn,
May 28-31, 2006 |
Citation
format: |
Vachkov, G. (2006). Fault Diagnosis of
Complex Systems Based on Modular Knowledge Base and Information Compression.
ECMS 2006 Proceedings edited by: W. Borutzky, A. Orsoni, R. Zobel
(pp. 112-117). European Council for Modeling and Simulation. doi:10.7148/2006-0112 |
DOI: |
http://dx.doi.org/10.7148/2006-0112 |
Abstract: |
A fault
diagnosis method for complex dynamic processes and systems is proposed in the
paper. It uses a special modular knowledge base, which is a collection of
modules for some typical faulty and normal conditions of the system. Each
module is considered as a kind of compressed information model, which
keeps in a compact form the most representative characteristics of the
original data, collected for the concrete system condition. Here a modified
version of the neural-gas learning algorithm for creation of all compressed
information models is proposed in the paper, where a preliminary assumed
number of neurons is used. The collected data from the current operation of the
system are also transformed into a respective compressed information model by
the same learning algorithm. The proposed fault diagnosis method is an
evaluation procedure for the similarity degree between the compressed
information model for the current operation and all the modules form the knowledge
base. Here three different measures of similarity are used, with the “center-of-gravity distance” showing the best results. Real
experimental data taken from different operations of a hydraulic excavator
are used in the paper to analyse and prove the
applicability of the proposed fault diagnosis method. |
Full
text: |