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

Zheng Liu, Wenping Cao, Po-Hsu Huang, Gui Yun Tian, James L. Kirtley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-5090-4168-8
DOIs
Publication statusPublished - 10 Nov 2016
Event2016 IEEE Power and Energy Society General Meeting - Boston, United States
Duration: 17 Jul 201621 Jul 2016

Publication series

Name
ISSN (Print)1944-9933

Meeting

Meeting2016 IEEE Power and Energy Society General Meeting
Abbreviated titlePESGM 2016
CountryUnited States
CityBoston
Period17/07/1621/07/16

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Keywords

  • condition monitoring
  • induction machine
  • stray flux
  • winding failures

Cite this

Liu, Z., Cao, W., Huang, P-H., Tian, G. Y., & Kirtley, J. L. (2016). Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 IEEE. https://doi.org/10.1109/PESGM.2016.7741486