Search:
Computing and Library Services - delivering an inspiring information environment

Detection and Diagnosis of Compound Faults in Induction Motors Using Electric Signals from Variable Speed Drives

Shaeboub, Abdulkarim, Lane, Mark, Haba, Usama, Gu, Fengshou and Ball, Andrew (2016) Detection and Diagnosis of Compound Faults in Induction Motors Using Electric Signals from Variable Speed Drives. In: The 22nd International Conference on Automation and Computing: ICAC 2016, 7th-8th September 2016, Essex, UK. (In Press)

[img]
Preview
PDF - Accepted Version
Download (800kB) | Preview

Abstract

As a primer driver, induction motors are the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal statues and avoid excessive energy consumption and severe damage to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar faults under closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives more correct indication of the fault severity and location for all operating conditions.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
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 > 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
Related URLs:
Depositing User: Sara Taylor
Date Deposited: 22 Sep 2016 14:23
Last Modified: 01 Dec 2016 02:48
URI: http://eprints.hud.ac.uk/id/eprint/29508

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©