Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature

Yu Lin; Zhexiang Zou; Dongqin Li; Huanqing Han; Bing Li; Yuzhuo Song; Nannan Lin; Fengshou Gu

doi:10.1007/978-3-031-49421-5_9

Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature

Yu Lin, Zhexiang Zou, Dongqin Li, Huanqing Han, Bing Li, Yuzhuo Song, Nannan Lin, Fengshou Gu

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

Abstract

Robotic joints, which are critical fixtures consisting of electrical motors, high-speed planetary gears, and low-speed cycloid wheels, are prone to friction and wear. Early fault detection and diagnosis of joint conditions are fundamental to providing relevant information for implementing maintenance. In this study, a motor current signature analysis (MCSA)-based method is proposed for monitoring and diagnosing the early faults in industrial robots. The research leverages the characteristics of motor current changes related to load variations, introducing time–frequency analysis (TFA) for early fault detection. The Fourier synchro squeezed transform (FSST) method provides energy-concentrated time–frequency representations, effectively capturing changes in robotic joint motion states through extracted time–frequency ridge features. Experimental studies were carried out based on a six-degree-of-freedom industrial robot with an eccentric mass on robot arms to simulate the early fault of joint frictional and wear. TFA results and corresponding features allow accurate fault magnitude and location resolution, demonstrating the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
Editors	Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
Publisher	Springer, Cham
Pages	109-122
Number of pages	14
Volume	152
ISBN (Electronic)	9783031494215
ISBN (Print)	9783031494208, 9783031494239
DOIs	https://doi.org/10.1007/978-3-031-49421-5_9
Publication status	Published - 29 May 2024
Event	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sep 2023 https://unified2023.org/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	152 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated title	UNIfied 2023
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23
Internet address	https://unified2023.org/

UN SDGs

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

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Cite this

Lin, Y., Zou, Z., Li, D., Han, H., Li, B., Song, Y., Lin, N., & Gu, F. (2024). Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature. In A. D. Ball, H. Ouyang, J. K. Sinha, & Z. Wang (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2 (Vol. 152, pp. 109-122). (Mechanisms and Machine Science; Vol. 152 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-49421-5_9

Lin, Yu ; Zou, Zhexiang ; Li, Dongqin et al. / Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. editor / Andrew D. Ball ; Huajiang Ouyang ; Jyoti K. Sinha ; Zuolu Wang. Vol. 152 Springer, Cham, 2024. pp. 109-122 (Mechanisms and Machine Science).

@inproceedings{f669d9edd75e4c07b8855bf22b9ac059,

title = "Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature",

abstract = "Robotic joints, which are critical fixtures consisting of electrical motors, high-speed planetary gears, and low-speed cycloid wheels, are prone to friction and wear. Early fault detection and diagnosis of joint conditions are fundamental to providing relevant information for implementing maintenance. In this study, a motor current signature analysis (MCSA)-based method is proposed for monitoring and diagnosing the early faults in industrial robots. The research leverages the characteristics of motor current changes related to load variations, introducing time–frequency analysis (TFA) for early fault detection. The Fourier synchro squeezed transform (FSST) method provides energy-concentrated time–frequency representations, effectively capturing changes in robotic joint motion states through extracted time–frequency ridge features. Experimental studies were carried out based on a six-degree-of-freedom industrial robot with an eccentric mass on robot arms to simulate the early fault of joint frictional and wear. TFA results and corresponding features allow accurate fault magnitude and location resolution, demonstrating the effectiveness of the proposed method.",

keywords = "Early fault detection, Motor current signature analysis, Robotic joints, Time–frequency analysis",

author = "Yu Lin and Zhexiang Zou and Dongqin Li and Huanqing Han and Bing Li and Yuzhuo Song and Nannan Lin and Fengshou Gu",

note = "Funding Information: The support for this research under the Guangdong Basic and Applied Basic Research Fund Offshore Wind Power Scheme-General Project (2022A1515240042), Open Fund Project of Key Laboratory of Science and Technology on Integrated Logistics Support (20210804023) and Special Projects in Key Areas in Fundamental and Foundational Applied Research of Guangdong Provincial Education Department (2021ZDZX1072) are gratefully acknowledged. Without their financial support, this work would not have been possible. Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, UNIfied 2023 ; Conference date: 29-08-2023 Through 01-09-2023",

year = "2024",

month = may,

day = "29",

doi = "10.1007/978-3-031-49421-5_9",

language = "English",

isbn = "9783031494208",

volume = "152",

series = "Mechanisms and Machine Science",

publisher = "Springer, Cham",

pages = "109--122",

editor = "Ball, {Andrew D.} and Huajiang Ouyang and Sinha, {Jyoti K.} and Zuolu Wang",

booktitle = "Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2",

address = "Switzerland",

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Lin, Y, Zou, Z, Li, D, Han, H, Li, B, Song, Y, Lin, N & Gu, F 2024, Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature. in AD Ball, H Ouyang, JK Sinha & Z Wang (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. vol. 152, Mechanisms and Machine Science, vol. 152 MMS, Springer, Cham, pp. 109-122, The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences, Huddersfield, United Kingdom, 29/08/23. https://doi.org/10.1007/978-3-031-49421-5_9

Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature. / Lin, Yu; Zou, Zhexiang; Li, Dongqin et al.

Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. ed. / Andrew D. Ball; Huajiang Ouyang; Jyoti K. Sinha; Zuolu Wang. Vol. 152 Springer, Cham, 2024. p. 109-122 (Mechanisms and Machine Science; Vol. 152 MMS).

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

TY - GEN

T1 - Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature

AU - Lin, Yu

AU - Zou, Zhexiang

AU - Li, Dongqin

AU - Han, Huanqing

AU - Li, Bing

AU - Song, Yuzhuo

AU - Lin, Nannan

AU - Gu, Fengshou

N1 - Funding Information: The support for this research under the Guangdong Basic and Applied Basic Research Fund Offshore Wind Power Scheme-General Project (2022A1515240042), Open Fund Project of Key Laboratory of Science and Technology on Integrated Logistics Support (20210804023) and Special Projects in Key Areas in Fundamental and Foundational Applied Research of Guangdong Provincial Education Department (2021ZDZX1072) are gratefully acknowledged. Without their financial support, this work would not have been possible. Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024/5/29

Y1 - 2024/5/29

N2 - Robotic joints, which are critical fixtures consisting of electrical motors, high-speed planetary gears, and low-speed cycloid wheels, are prone to friction and wear. Early fault detection and diagnosis of joint conditions are fundamental to providing relevant information for implementing maintenance. In this study, a motor current signature analysis (MCSA)-based method is proposed for monitoring and diagnosing the early faults in industrial robots. The research leverages the characteristics of motor current changes related to load variations, introducing time–frequency analysis (TFA) for early fault detection. The Fourier synchro squeezed transform (FSST) method provides energy-concentrated time–frequency representations, effectively capturing changes in robotic joint motion states through extracted time–frequency ridge features. Experimental studies were carried out based on a six-degree-of-freedom industrial robot with an eccentric mass on robot arms to simulate the early fault of joint frictional and wear. TFA results and corresponding features allow accurate fault magnitude and location resolution, demonstrating the effectiveness of the proposed method.

AB - Robotic joints, which are critical fixtures consisting of electrical motors, high-speed planetary gears, and low-speed cycloid wheels, are prone to friction and wear. Early fault detection and diagnosis of joint conditions are fundamental to providing relevant information for implementing maintenance. In this study, a motor current signature analysis (MCSA)-based method is proposed for monitoring and diagnosing the early faults in industrial robots. The research leverages the characteristics of motor current changes related to load variations, introducing time–frequency analysis (TFA) for early fault detection. The Fourier synchro squeezed transform (FSST) method provides energy-concentrated time–frequency representations, effectively capturing changes in robotic joint motion states through extracted time–frequency ridge features. Experimental studies were carried out based on a six-degree-of-freedom industrial robot with an eccentric mass on robot arms to simulate the early fault of joint frictional and wear. TFA results and corresponding features allow accurate fault magnitude and location resolution, demonstrating the effectiveness of the proposed method.

KW - Early fault detection

KW - Motor current signature analysis

KW - Robotic joints

KW - Time–frequency analysis

UR - http://www.scopus.com/inward/record.url?scp=85195545520&partnerID=8YFLogxK

UR - https://doi.org/10.1007/978-3-031-49421-5

U2 - 10.1007/978-3-031-49421-5_9

DO - 10.1007/978-3-031-49421-5_9

M3 - Conference contribution

AN - SCOPUS:85195545520

SN - 9783031494208

SN - 9783031494239

VL - 152

T3 - Mechanisms and Machine Science

SP - 109

EP - 122

BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2

A2 - Ball, Andrew D.

A2 - Ouyang, Huajiang

A2 - Sinha, Jyoti K.

A2 - Wang, Zuolu

PB - Springer, Cham

T2 - The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences

Y2 - 29 August 2023 through 1 September 2023

ER -

Lin Y, Zou Z, Li D, Han H, Li B, Song Y et al. Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature. In Ball AD, Ouyang H, Sinha JK, Wang Z, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2. Vol. 152. Springer, Cham. 2024. p. 109-122. (Mechanisms and Machine Science). Epub 2024 May 28. doi: 10.1007/978-3-031-49421-5_9

Early Fault Detection of Robotic Joints Based on Time–Frequency Analysis of Motor Current Signature

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