Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2011.5975545
Title: A feature based frequency domain analysis algorithm for fault detection of induction motors
Authors: Wang, Z.
Chang, C.S. 
Zhang, Y.
Keywords: Fast Fourier Transform
Fault detection
Independent Component Analysis
Induction motors fed from inverter
Issue Date: 2011
Source: Wang, Z.,Chang, C.S.,Zhang, Y. (2011). A feature based frequency domain analysis algorithm for fault detection of induction motors. Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011 : 27-32. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIEA.2011.5975545
Abstract: This paper studies the stator currents collected from several inverter-fed laboratory induction motors and proposes a new feature based frequency domain analysis method for performing the detection of induction motor faults, such as the broken rotor-bar or bearing fault. The mathematical formulation is presented to calculate the features, which are called FFT-ICA features in this paper. The obtained FFT-ICA features are normalized by using healthy motor as benchmarks to establish a feature database for fault detection. Compare with conventional frequency-domain analysis method, no prior knowledge of the motor parameters or other measurements are required for calculating features. Only one phase stator current waveforms are enough to provide consistent diagnosis of inverter-fed induction motors at different frequencies. The proposed method also outperforms our previous time domain analysis method. © 2011 IEEE.
Source Title: Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011
URI: http://scholarbank.nus.edu.sg/handle/10635/68800
ISBN: 9781424487554
DOI: 10.1109/ICIEA.2011.5975545
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

7
checked on Dec 11, 2017

Page view(s)

35
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.