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Machine Performance and Condition Monitoring Through Data Mining and Database Optimization

Baqqar, Mabrouka, Gu, Fengshou and Lu, Joan (2010) Machine Performance and Condition Monitoring Through Data Mining and Database Optimization. In: Future Technologies in Computing and Engineering: Proceedings of Computing and Engineering Annual Researchers' Conference 2010: CEARC’10. University of Huddersfield, Huddersfield, p. 186. ISBN 9781862180932

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Abstract

Engineering dataset has growing rapidly in size and diversities with data acquisition technology development in recent years. However the full use of the datasets for maximizing machine operation and design has not been investigated systematically because of the complexity of datasets and large scale of data amount. This also means that traditional statistical data analysis methods are no longer the efficient methods to obtain useful knowledge from the dataset. Therefore this study will focus on applying more advanced data minting technologies and optimizing database systems so that more accurate and efficient knowledge can be extracted from engineering datasets for machine performance and condition monitoring. For the first year study, a full understanding engineering dataset has been obtained based on the condition monitoring activities in the DERG laboratory.
In the mean time a review has also conducted on different techniques such as Neural networks, clustering, genetic algorithms, decision trees and support vector machines that are used widely for data mining. Moreover, to evaluate the effectiveness of using the techniques, a prototype
database has been developed and applied with the methods. The results obtained from this evaluation study have shown that the data mining can be efficient to obtain information for condition monitoring. But more work on developing methods to optimize both the parameters of using
the methods and the database organization.

Item Type: Book Chapter
Uncontrolled Keywords: condition monitoring, data mining techniques, Database Optimization.
Subjects: T Technology > T Technology (General)
T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Computing and Engineering Annual Researchers' Conference (CEARC)
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
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Depositing User: Sharon Beastall
Date Deposited: 14 Jan 2011 10:35
Last Modified: 24 Aug 2015 22:05
URI: http://eprints.hud.ac.uk/id/eprint/9337

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