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Investigation of Motor Supply Signature Analysis to Detect Motor Resistance Imbalances

Lane, M., Ashari, Djoni, Ball, Andrew and Gu, Fengshou (2015) Investigation of Motor Supply Signature Analysis to Detect Motor Resistance Imbalances. In: Proceedings of the 21st International Conference on Automation and Computing (ICAC). IEEE. ISBN 978-0-9926801-0-7

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Abstract

The trend to use inverter drives in industry is well established. It is desirable to monitor the condition of the motor/drive combination with the minimum of system intervention and at the same time retaining compatibility with the latest generation of AC PWM vector drives. This paper studies the effect of stator resistance asymmetry on the performance of the motor driven by a latest-generation unmodified AC PWM drive under varying speed conditions. The asymmetry of increased resistance in one phase is intended to simulate the onset of a failing connection between drive and motor but one that is non-critical and will remain undetected in use because the resistance increase is small and does not appear to affect the motor operation significantly. The performance is compared against baseline motor data for the resistance increase. Moreover, it is also examined following an auto-tune on the drive with the asymmetric motor in order to observe if any effects of resistance imbalance can be shown on the sensorless vector control algorithms. Initial results from the motor tests clearly show a difference in values measured from the motor current and voltage signals, which can be a useful indication of the asymmetry of the drive system.

Item Type: Book Chapter
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
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
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Depositing User: Cherry Edmunds
Date Deposited: 05 Oct 2015 13:33
Last Modified: 01 Dec 2016 08:57
URI: http://eprints.hud.ac.uk/id/eprint/26004

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