Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation

Joshua Hoole; Ria Mitchell; Dominic J North; Nick Simpson; Phil H Mellor

doi:10.1109/IECON49645.2022.9968418

Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation

Joshua Hoole, Ria Mitchell, Dominic J North, Nick Simpson, Phil H Mellor

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

90 Downloads (Pure)

Abstract

Compressed windings can substantively increase the power density of electrical machines. However, the compression of coils can lead to significant deformation of the conductor lay, cross-sections and insulation coating, impacting the longevity, loss and thermal performance of such windings. This paper presents the use of high resolution X-ray Computed Tomography (XCT), along with 2D and 3D image segmentation techniques, to perform an initial characterisation of a compressed aluminium winding. From the XCT derived data, significant localised conductor deformation and strand insulation thinning has been observed.

Original language	English
Title of host publication	IECON 2022
Subtitle of host publication	48th Annual Conference of the IEEE Industrial Electronics Society
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	6
ISBN (Electronic)	978-1-6654-8025-3
ISBN (Print)	978-1-6654-8026-0
DOIs	https://doi.org/10.1109/IECON49645.2022.9968418
Publication status	Published - 9 Dec 2022
Event	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society - Brussels, Belgium Duration: 17 Oct 2022 → 20 Oct 2022

Publication series

Name	Annual Conference of Industrial Electronics Society
Publisher	IEEE
ISSN (Print)	1553-572X
ISSN (Electronic)	2577-1647

Conference

Conference	IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
Country/Territory	Belgium
City	Brussels
Period	17/10/22 → 20/10/22

Bibliographical note

Funding Information:
This work was funded by the EPSRC Impact Acceleration Account Net Zero Fund. The authors acknowledge the University of Sheffield Tomography Centre (STC) funding from EPSRC (EP/T006390/1).

Publisher Copyright:
© 2022 IEEE.

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10.1109/IECON49645.2022.9968418

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Hoole, J., Mitchell, R., North, D. J., Simpson, N., & Mellor, P. H. (2022). Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation. In IECON 2022: 48th Annual Conference of the IEEE Industrial Electronics Society (Annual Conference of Industrial Electronics Society). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IECON49645.2022.9968418

Hoole, Joshua ; Mitchell, Ria ; North, Dominic J et al. / Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation. IECON 2022: 48th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers (IEEE), 2022. (Annual Conference of Industrial Electronics Society).

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title = "Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation",

abstract = "Compressed windings can substantively increase the power density of electrical machines. However, the compression of coils can lead to significant deformation of the conductor lay, cross-sections and insulation coating, impacting the longevity, loss and thermal performance of such windings. This paper presents the use of high resolution X-ray Computed Tomography (XCT), along with 2D and 3D image segmentation techniques, to perform an initial characterisation of a compressed aluminium winding. From the XCT derived data, significant localised conductor deformation and strand insulation thinning has been observed.",

author = "Joshua Hoole and Ria Mitchell and North, \{Dominic J\} and Nick Simpson and Mellor, \{Phil H\}",

note = "Funding Information: This work was funded by the EPSRC Impact Acceleration Account Net Zero Fund. The authors acknowledge the University of Sheffield Tomography Centre (STC) funding from EPSRC (EP/T006390/1). Publisher Copyright: {\textcopyright} 2022 IEEE.; IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 17-10-2022 Through 20-10-2022",

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Hoole, J, Mitchell, R, North, DJ , Simpson, N & Mellor, PH 2022, Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation. in IECON 2022: 48th Annual Conference of the IEEE Industrial Electronics Society. Annual Conference of Industrial Electronics Society, Institute of Electrical and Electronics Engineers (IEEE), IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 17/10/22. https://doi.org/10.1109/IECON49645.2022.9968418

Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation. / Hoole, Joshua; Mitchell, Ria; North, Dominic J et al.
IECON 2022: 48th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers (IEEE), 2022. (Annual Conference of Industrial Electronics Society).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation

AU - Hoole, Joshua

AU - Mitchell, Ria

AU - North, Dominic J

AU - Simpson, Nick

AU - Mellor, Phil H

N1 - Funding Information: This work was funded by the EPSRC Impact Acceleration Account Net Zero Fund. The authors acknowledge the University of Sheffield Tomography Centre (STC) funding from EPSRC (EP/T006390/1). Publisher Copyright: © 2022 IEEE.

PY - 2022/12/9

Y1 - 2022/12/9

N2 - Compressed windings can substantively increase the power density of electrical machines. However, the compression of coils can lead to significant deformation of the conductor lay, cross-sections and insulation coating, impacting the longevity, loss and thermal performance of such windings. This paper presents the use of high resolution X-ray Computed Tomography (XCT), along with 2D and 3D image segmentation techniques, to perform an initial characterisation of a compressed aluminium winding. From the XCT derived data, significant localised conductor deformation and strand insulation thinning has been observed.

AB - Compressed windings can substantively increase the power density of electrical machines. However, the compression of coils can lead to significant deformation of the conductor lay, cross-sections and insulation coating, impacting the longevity, loss and thermal performance of such windings. This paper presents the use of high resolution X-ray Computed Tomography (XCT), along with 2D and 3D image segmentation techniques, to perform an initial characterisation of a compressed aluminium winding. From the XCT derived data, significant localised conductor deformation and strand insulation thinning has been observed.

U2 - 10.1109/IECON49645.2022.9968418

DO - 10.1109/IECON49645.2022.9968418

M3 - Conference Contribution (Conference Proceeding)

SN - 978-1-6654-8026-0

T3 - Annual Conference of Industrial Electronics Society

BT - IECON 2022

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society

Y2 - 17 October 2022 through 20 October 2022

ER -

Hoole J, Mitchell R, North DJ , Simpson N , Mellor PH. Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation. In IECON 2022: 48th Annual Conference of the IEEE Industrial Electronics Society. Institute of Electrical and Electronics Engineers (IEEE). 2022. (Annual Conference of Industrial Electronics Society). doi: 10.1109/IECON49645.2022.9968418

Characterisation of Compressed Windings via High Resolution X-ray Computed Tomography and Semi-Automatic Segmentation

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