Non-invasive load monitoring of induction motor drives using magnetic flux sensors


Existing load monitoring methods for induction machines are generally effective, but suffer from sensitivity problems at low speeds and non-linearity problems at high supply frequencies. This study proposes a new noninvasive load monitoring method based on giant magnetoresistance flux sensors to trace stray flux leaking from induction motors. Finite element analysis is applied to analyse stray flux features of test machines. Contrary to the conventional methods of measuring stator and/or rotator rotor voltage and current, the proposed method measures the dynamic magnetic field at specific locations and provides time-spectrum features (e.g. spectrograms), response time load and stator/rotor characteristics. Three induction motors with different starting loading profiles are tested at two separate test benches and their results are analysed in the time-frequency domain. Their steady features and dynamic load response time through spectrograms under variable loads are extracted to correlate with load variations based on spectrogram information. In addition, the transient stray flux spectrogram and time information are more effective for load monitoring than steady state information from numerical and experimental studies. The proposed method is proven to be a low-cost and non-invasive method for induction machine load monitoring.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
Additional Information: This paper is a postprint of a paper submitted to and accepted for publication in IET Power Electronics and is subject to Institution of Engineering and technology Copyright. The copy of the records is available at the IET Digital Library. Funding: FP7 HEMOW Project (FP7-PEOPLE-2010-IRSES, 269202) and EPSRC Project (EP/K008552/3).
Uncontrolled Keywords: Electrical and Electronic Engineering
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-02-10
Accepted Date: 2016-07-13
Submitted Date: 2016-04-26
Authors: Liu, Zheng
Tian, Guiyun
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)
Dai, Xuewu
Shaw, Brian
Lambert, Robert



Version: Accepted Version

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