Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors


Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.

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Divisions: College of Engineering & Physical Sciences
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Event Title: 2016 IEEE Power and Energy Society General Meeting
Event Type: Other
Event Dates: 2016-07-17 - 2016-07-21
Uncontrolled Keywords: condition monitoring,induction machine,stray flux,winding failures,Energy Engineering and Power Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering
ISBN: 978-1-5090-4168-8
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-11-10
Accepted Date: 2016-11-10
Authors: Liu, Zheng
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)
Huang, Po-Hsu
Tian, Gui Yun
Kirtley, James L.



Version: Accepted Version

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