Machine condition monitoring is gaining importance in industry due to the
need to increase machine reliability and decrease the possible loss of production
due to machine breakdown. Often the data available to build a condition
monitoring system does not fully represent the system. It is also often common
that the data becomes available in small batches over a period of time. Hence,
it is important to build a system that is able to accommodate new data as
it becomes available without compromising the performance of the previously
learned data. In real-world applications, more than one condition monitoring
technology is used to monitor the condition of a machine. This leads to large
amounts of data, which require a highly skilled diagnostic specialist to analyze.
In this thesis, artificial intelligence (AI) techniques are used to build a
condition monitoring system that has incremental learning capabilities. Two
incremental learning algorithms are implemented, the first method uses Fuzzy
ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. In addition,
intelligent agents and multi-agent systems are used to build a condition
monitoring system that is able to accommodate various analysis techniques.
Experimentation was performed on two sets of condition monitoring data; the
dissolved gas analysis (DGA) data obtained from high voltage bushings and the
vibration data obtained from motor bearing. Results show that both Learn++
and FAM are able to accommodate new data without compromising the performance
of classifiers on previously learned information. Results also show
that intelligent agent and multi-agent system are able to achieve modularity
and flexibility.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/5482 |
| Date | 20 August 2008 |
| Creators | Vilakazi, Christina Busisiwe |
| Source Sets | South African National ETD Portal |
| Language | Spanish |
| Detected Language | English |
| Type | Thesis |
| Format | 768855 bytes, application/pdf, application/pdf |
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