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The Detection of Shaft Misalignments using Motor Current Signals from a Sensorless Variable Speed Drive

Abusaad, Samieh, Benghozzi, Ahmed, Smith, Ann, Gu, Fengshou and Ball, Andrew (2015) The Detection of Shaft Misalignments using Motor Current Signals from a Sensorless Variable Speed Drive. In: Vibration Engineering and Technology of Machinery. Mechanisms and Machine Science (23). Springer, London, pp. 173-183. ISBN 978-3-319-09918-7

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Shaft misalignments are common problems in rotating machines which cause additional dynamic and static loads, and vibrations in the system, leading to early damages and energy loss. It has been shown previously that it is possible to use motor current signature analysis to detect and diagnose this fault in motor drives. However, with a variable speed drive (VSD) system, it becomes dif-ficult to detect faults as the drive compensates for the small changes from fault ef-fects and increased noise in the measured data. In this paper, motor current signa-tures including dynamic and static data have been investigated for misalignment diagnosis in a VSD system. The study has made a systemic comparison of differ-ent control parameters between two common operation modes: open loop and sen-sorless control. Results show that fault detection features on the motor current from the sensorless mode can be the same as those of the open loop mode, however, the detection and diagnosis is significantly more difficult. In contrast, because of the additional frictional load, features from static data show results of early detection and diagnosis of different degrees of misalignment is as good as that from conventional vibration methods.

Item Type: Book Chapter
Additional Information: Proceedings of VETOMAC X 2014, held at the University of Manchester, UK, September 9-11, 2014
Uncontrolled Keywords: Sensorless VSD,Misalignment, Current Signature, Condition Monitoring,Vibration
Subjects: T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TS Manufactures
Schools: School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
Depositing User: Samieh Abu Saad
Date Deposited: 03 Oct 2014 15:58
Last Modified: 28 Aug 2021 18:53


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