Motor current signature analysis (MCSA) is an important, reliable and non-invasive technique for monitoring rotation machines. Spectrum analysis is a common way to implement MCSA, which allows large faults such as severe mechanical imbalance to be extracted successfully, but is often ineffective in the detection of incipient faults such as supporting bearings from motor drive systems because of noise and nonlinear interferences. To improve the performance of MSCA, this paper exploits the use of Empirical Mode Decomposition (EMD) method as an advanced tool to process motor current signals for noise reduction and nonlinear signature enhancement. The nonlinear demodulation property of EMD is firstly reviewed in association with the motor current signal models with fault cases. Then EMD is applied to signals from different fault cases from a centrifuge pump system to verify its performances in extracting the fault signatures for separating different faults. In conjunction with the envelope spectrum of separated intrinsic mode function (IMF), it shows that the proposed EMD based approach produces a better result in diagnosing common pump faults: small defects on impeller and bearings, which cannot be separated based on spectrum analysis.
|Title of host publication
|2018 24th IEEE International Conference on Automation and Computing (ICAC)
|Subtitle of host publication
|Improving Productivity through Automation and Computing
|Institute of Electrical and Electronics Engineers Inc.
|Published - 1 Jul 2019
|24th IEEE International Conference on Automation and Computing: Improving Productivity through Automation and Computing - Newcastle University, Newcastle upon Tyne, United Kingdom
Duration: 6 Sep 2018 → 7 Sep 2018
Conference number: 24
https://ieeexplore.ieee.org/xpl/conhome/8742895/proceeding (Website Containing the Proceedings)
http://www.cacsuk.co.uk/index.php/conferences/icac (Link to Conference Information)
|24th IEEE International Conference on Automation and Computing
|Newcastle upon Tyne
|6/09/18 → 7/09/18