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

doi:10.1109/PESGM.2016.7741486

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

College of Engineering and Physical Sciences

Newcastle University
Massachusetts Institute of Technology

Research output: Chapter in Book/Published conference output › Conference publication

18 Citations (Scopus)

114 Downloads (Pure)

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 language	English
Title of host publication	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-5090-4168-8
DOIs	https://doi.org/10.1109/PESGM.2016.7741486
Publication status	Published - 10 Nov 2016
Event	2016 IEEE Power and Energy Society General Meeting - Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016

Publication series

Name
ISSN (Print)	1944-9933

Meeting

Meeting	2016 IEEE Power and Energy Society General Meeting
Abbreviated title	PESGM 2016
Country/Territory	United States
City	Boston
Period	17/07/16 → 21/07/16

Bibliographical note

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

condition monitoring
induction machine
stray flux
winding failures

Access to Document

10.1109/PESGM.2016.7741486

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 610 KB

Cite this

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title = "Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors",

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.",

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N1 - © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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N2 - 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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ER -

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

Abstract

Publication series

Meeting

Bibliographical note

UN SDGs

Keywords

Access to Document

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