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Artificial intelligence-based condition monitoring for practical electrical drives

Ashari, Djoni, Pislaru, Crinela, Ball, Andrew and Gu, Fengshou (2012) Artificial intelligence-based condition monitoring for practical electrical drives. In: Proceedings of The Queen’s Diamond Jubilee Computing and Engineering Annual Researchers’ Conference 2012: CEARC’12. University of Huddersfield, Huddersfield, p. 143. ISBN 978-1-86218-106-9

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        Abstract

        The main types of existing Condition Monitoring methods (MCSA, GA, IAS) for electrical drives are
        described. Then the steps for the design of expert systems are presented: problem identification and analysis, system specification, development tool selection, knowledge based, prototyping and testing. The employment of SOMA (Self-Organizing Migrating Algorithm) used for the optimization of ambient
        vibration energy harvesting is analyzed. The power electronics devices are becoming smaller in size and consume less power so they are well suited for ambient vibration conversion systems for charging batteries or supplying power directly. The springless resonance mechanism has a mechanical part (mass, spring, damper), an electromagnetic energy converter (coils) and electrical load. SOMA is an artificial intelligence algorithm which is used to find the best combination of independent parameters to
        optimize the device and obtain the maximum amount of electrical power. Future research will be done
        to improve the quality factor of the model and to use it for a new harvester design for wireless
        applications.

        Item Type: Book Chapter
        Uncontrolled Keywords: artificial intelligence, condition monitoring system, electrical drives, expert system, energy harvesting
        Subjects: T Technology > TA Engineering (General). Civil engineering (General)
        Schools: School of Computing and Engineering
        School of Computing and Engineering > Automotive Engineering Research Group
        School of Computing and Engineering > Centre for Precision Technologies
        School of Computing and Engineering > Centre for Precision Technologies > Engineering Control and Machine Performance Research Group
        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 > Energy, Emissions and the Environment Research Group
        School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
        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 > Pedagogical Research Group
        Related URLs:
        Depositing User: Sharon Beastall
        Date Deposited: 03 May 2012 09:50
        Last Modified: 23 Dec 2013 11:12
        URI: http://eprints.hud.ac.uk/id/eprint/13475

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