A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields

Yan Ma; Jianjiao Deng; Bin Li; Tongjie Shi; Xue Ao; Siyu Zhou; Shengsen Zhou; Huan Ouyang; Conggan Ma

doi:10.1049/icp.2025.3360

A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields

Yan Ma
, Jianjiao Deng
, Bin Li
, Tongjie Shi
, Xue Ao
, Siyu Zhou
, Shengsen Zhou
, Huan Ouyang
, Conggan Ma^*

^*Corresponding author for this work

FAW Group Corporation
National Key Laboratory of Advanced Vehicle Integration and Control
Harbin Institute of Technology Weihai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Existing data-driven methods for mixed eccentricity (ME) fault diagnosis in permanent magnet synchronous motors (PMSMs) face the challenge of limited large-scale labeled datasets. This paper proposes a novel simulation-driven approach for diagnosing mixed eccentricity faults in permanent magnet synchronous motors (PMSMs) using stray magnetic field data. By leveraging a finite element model and radial basis function neural network, our method achieves accurate predictions of eccentricity parameters. The results demonstrate the effectiveness of our approach, providing a non-intrusive and simulation-based solution for fault diagnosis in PMSMs. This study contributes to the growing field of predictive maintenance and Industry 4.0, enabling more efficient and reliable motor operation.

Original language	English
Title of host publication	8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025
Publisher	Institution of Engineering and Technology
Pages	710-717
Number of pages	8
Volume	2025
Edition	30
ISBN (Electronic)	9781807050207, 9781807050344, 9781807050351, 9781807050375, 9781837242634, 9781837242900, 9781837242917, 9781837243143, 9781837243150, 9781837243167, 9781837243235, 9781837243341, 9781837243358, 9781837245277, 9781837246847, 9781837246854, 9781837247004, 9781837247011, 9781837247028, 9781837247035, 9781837247042, 9781837247059, 9781837247257, 9781837247264, 9781837247271, 9781837247295, 9781837247325, 9781837247332, 9781837249916
DOIs	https://doi.org/10.1049/icp.2025.3360
State	Published - 1 Dec 2025
Externally published	Yes
Event	8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025 - Taiyuan, China Duration: 12 Jul 2025 → 14 Jul 2025

Conference

Conference	8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025
Country/Territory	China
City	Taiyuan
Period	12/07/25 → 14/07/25

Keywords

Mixed eccentricity
PMSM
simulated data-driven
stray magnetic fields

Access to Document

10.1049/icp.2025.3360

Cite this

Ma, Y., Deng, J., Li, B., Shi, T., Ao, X., Zhou, S., Zhou, S., Ouyang, H., & Ma, C. (2025). A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields. In 8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025 (30 ed., Vol. 2025, pp. 710-717). Institution of Engineering and Technology. https://doi.org/10.1049/icp.2025.3360

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title = "A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields",

abstract = "Existing data-driven methods for mixed eccentricity (ME) fault diagnosis in permanent magnet synchronous motors (PMSMs) face the challenge of limited large-scale labeled datasets. This paper proposes a novel simulation-driven approach for diagnosing mixed eccentricity faults in permanent magnet synchronous motors (PMSMs) using stray magnetic field data. By leveraging a finite element model and radial basis function neural network, our method achieves accurate predictions of eccentricity parameters. The results demonstrate the effectiveness of our approach, providing a non-intrusive and simulation-based solution for fault diagnosis in PMSMs. This study contributes to the growing field of predictive maintenance and Industry 4.0, enabling more efficient and reliable motor operation.",

keywords = "Mixed eccentricity, PMSM, simulated data-driven, stray magnetic fields",

author = "Yan Ma and Jianjiao Deng and Bin Li and Tongjie Shi and Xue Ao and Siyu Zhou and Shengsen Zhou and Huan Ouyang and Conggan Ma",

note = "Publisher Copyright: {\textcopyright} The Institution of Engineering \& Technology 2025.; 8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025 ; Conference date: 12-07-2025 Through 14-07-2025",

year = "2025",

month = dec,

day = "1",

doi = "10.1049/icp.2025.3360",

language = "英语",

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Ma, Y, Deng, J, Li, B, Shi, T, Ao, X, Zhou, S, Zhou, S, Ouyang, H & Ma, C 2025, A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields. in 8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025. 30 edn, vol. 2025, Institution of Engineering and Technology, pp. 710-717, 8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025, Taiyuan, China, 12/07/25. https://doi.org/10.1049/icp.2025.3360

A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields. / Ma, Yan; Deng, Jianjiao; Li, Bin et al.
8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025. Vol. 2025 30. ed. Institution of Engineering and Technology, 2025. p. 710-717.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields

AU - Ma, Yan

AU - Deng, Jianjiao

AU - Li, Bin

AU - Shi, Tongjie

AU - Ao, Xue

AU - Zhou, Siyu

AU - Zhou, Shengsen

AU - Ouyang, Huan

AU - Ma, Conggan

PY - 2025/12/1

Y1 - 2025/12/1

N2 - Existing data-driven methods for mixed eccentricity (ME) fault diagnosis in permanent magnet synchronous motors (PMSMs) face the challenge of limited large-scale labeled datasets. This paper proposes a novel simulation-driven approach for diagnosing mixed eccentricity faults in permanent magnet synchronous motors (PMSMs) using stray magnetic field data. By leveraging a finite element model and radial basis function neural network, our method achieves accurate predictions of eccentricity parameters. The results demonstrate the effectiveness of our approach, providing a non-intrusive and simulation-based solution for fault diagnosis in PMSMs. This study contributes to the growing field of predictive maintenance and Industry 4.0, enabling more efficient and reliable motor operation.

AB - Existing data-driven methods for mixed eccentricity (ME) fault diagnosis in permanent magnet synchronous motors (PMSMs) face the challenge of limited large-scale labeled datasets. This paper proposes a novel simulation-driven approach for diagnosing mixed eccentricity faults in permanent magnet synchronous motors (PMSMs) using stray magnetic field data. By leveraging a finite element model and radial basis function neural network, our method achieves accurate predictions of eccentricity parameters. The results demonstrate the effectiveness of our approach, providing a non-intrusive and simulation-based solution for fault diagnosis in PMSMs. This study contributes to the growing field of predictive maintenance and Industry 4.0, enabling more efficient and reliable motor operation.

KW - Mixed eccentricity

KW - PMSM

KW - simulated data-driven

KW - stray magnetic fields

UR - https://www.scopus.com/pages/publications/105038401482

U2 - 10.1049/icp.2025.3360

DO - 10.1049/icp.2025.3360

M3 - 会议稿件

AN - SCOPUS:105038401482

VL - 2025

SP - 710

EP - 717

BT - 8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025

PB - Institution of Engineering and Technology

T2 - 8th International Conference on Mechanical, Electric, and Industrial Engineering, MEIE 2025

Y2 - 12 July 2025 through 14 July 2025

ER -

A New Simulation-Driven Method for Diagnosing Mixed Eccentricity in PMSMs Using Stray Magnetic Fields

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