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Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

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Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System. / Zhu, Qingyun; Lu, Jingfeng; Wang, Xiaoxian et al.
In: IEEE Internet of Things Journal, Vol. 10, No. 8, 15.04.2023, p. 7393-7404.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Zhu, Q, Lu, J, Wang, X, Wang, H, Lu, S, de Silva, CW & Xia, M 2023, 'Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System', IEEE Internet of Things Journal, vol. 10, no. 8, pp. 7393-7404. https://doi.org/10.1109/jiot.2022.3228869

APA

Zhu, Q., Lu, J., Wang, X., Wang, H., Lu, S., de Silva, C. W., & Xia, M. (2023). Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System. IEEE Internet of Things Journal, 10(8), 7393-7404. https://doi.org/10.1109/jiot.2022.3228869

Vancouver

Zhu Q, Lu J, Wang X, Wang H, Lu S, de Silva CW et al. Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System. IEEE Internet of Things Journal. 2023 Apr 15;10(8):7393-7404. Epub 2022 Dec 14. doi: 10.1109/jiot.2022.3228869

Author

Zhu, Qingyun ; Lu, Jingfeng ; Wang, Xiaoxian et al. / Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System. In: IEEE Internet of Things Journal. 2023 ; Vol. 10, No. 8. pp. 7393-7404.

Bibtex

@article{acb49476a22746fba469ec164f8acd86,
title = "Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System",
abstract = "Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.",
keywords = "Computer Networks and Communications, Computer Science Applications, Hardware and Architecture, Information Systems, Signal Processing",
author = "Qingyun Zhu and Jingfeng Lu and Xiaoxian Wang and Hui Wang and Siliang Lu and {de Silva}, {Clarence W.} and Min Xia",
note = "{\textcopyright}2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2023",
month = apr,
day = "15",
doi = "10.1109/jiot.2022.3228869",
language = "English",
volume = "10",
pages = "7393--7404",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "8",

}

RIS

TY - JOUR

T1 - Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System

AU - Zhu, Qingyun

AU - Lu, Jingfeng

AU - Wang, Xiaoxian

AU - Wang, Hui

AU - Lu, Siliang

AU - de Silva, Clarence W.

AU - Xia, Min

N1 - ©2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2023/4/15

Y1 - 2023/4/15

N2 - Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

AB - Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

KW - Computer Networks and Communications

KW - Computer Science Applications

KW - Hardware and Architecture

KW - Information Systems

KW - Signal Processing

U2 - 10.1109/jiot.2022.3228869

DO - 10.1109/jiot.2022.3228869

M3 - Journal article

VL - 10

SP - 7393

EP - 7404

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 8

ER -