Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Zheng Liu, Dr Wenping Cao, Professor Gui Yun TianORCiD, Professor James Kirtley Jr


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 2016 IEEE. 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.

Publication metadata

Author(s): Liu Z, Cao W, Huang P-H, Tian G-Y, Kirtley JL

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Power and Energy Society Meeting (PESGM) 2016

Year of Conference: 2016

Online publication date: 14/11/2016

Acceptance date: 02/04/2016

Publisher: IEEE Computer Society


DOI: 10.1109/PESGM.2016.7741486

Library holdings: Search Newcastle University Library for this item

ISBN: 9781509041688