Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots

Samuel Ayankoso; Yang Guan; Fengshou Gu; Hassna Louadah; Hamidreza Fahham; Andrew Ball

doi:10.1007/978-3-031-93327-1_45

Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots

Samuel Ayankoso, Yang Guan, Fengshou Gu, Hassna Louadah, Hamidreza Fahham, Andrew Ball

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

Abstract

Amidst the rapidly advancing landscape of robotics, AI, and IoT, collaborative robots (cobots) play a pivotal role in the transition to Industry 5.0. The precise positioning and performance of cobots are crucial for ensuring product quality and worker safety. This paper examines system and subsystem-level Prognostics and Health Management (PHM) by leveraging Tool Centre Point (TCP) position and orientation data, along with joints’ currents measurements of cobots, to detect anomalies caused by functional deviations. The proposed framework employs multiple Long Short-Term Memory AutoEncoder (LSTM-AE) models, and the results obtained demonstrate that the framework is highly effective in detecting and isolating cobot anomalies, with high potential to be developed into a smart solution for identifying real faults.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences
Subtitle of host publication	UNIfied 2024—Volume 1
Editors	Maneesh Singh, Gunjan Soni, Jyoti Sinha, Andrew D. Ball, Fengshou Gu, Huajiang Ouyang, Carol Featherston
Publisher	Springer, Cham
Pages	723-738
Number of pages	16
Edition	1
ISBN (Electronic)	9783031933271
ISBN (Print)	9783031933264, 9783031933295
DOIs	https://doi.org/10.1007/978-3-031-93327-1_45
Publication status	Published - 4 Oct 2025
Event	UNIfied International Conference on Emerging Technologies in Cyber-Physical Systems and Industrial AI - Jaipur, India Duration: 26 Nov 2024 → 28 Nov 2024 https://mnit.ac.in/news/news?newsid=QK+M3Q== https://unified2024.netlify.app/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer, Cham
Volume	181 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	UNIfied International Conference on Emerging Technologies in Cyber-Physical Systems and Industrial AI
Abbreviated title	UNIfied 2024
Country/Territory	India
City	Jaipur
Period	26/11/24 → 28/11/24
Internet address	https://mnit.ac.in/news/news?newsid=QK+M3Q== https://unified2024.netlify.app/

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Cite this

Ayankoso, S., Guan, Y., Gu, F., Louadah, H., Fahham, H., & Ball, A. (2025). Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots. In M. Singh, G. Soni, J. Sinha, A. D. Ball, F. Gu, H. Ouyang, & C. Featherston (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024—Volume 1 (1 ed., pp. 723-738). (Mechanisms and Machine Science; Vol. 181 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-93327-1_45

Ayankoso, Samuel ; Guan, Yang ; Gu, Fengshou et al. / Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots. Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024—Volume 1. editor / Maneesh Singh ; Gunjan Soni ; Jyoti Sinha ; Andrew D. Ball ; Fengshou Gu ; Huajiang Ouyang ; Carol Featherston. 1. ed. Springer, Cham, 2025. pp. 723-738 (Mechanisms and Machine Science).

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title = "Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots",

abstract = "Amidst the rapidly advancing landscape of robotics, AI, and IoT, collaborative robots (cobots) play a pivotal role in the transition to Industry 5.0. The precise positioning and performance of cobots are crucial for ensuring product quality and worker safety. This paper examines system and subsystem-level Prognostics and Health Management (PHM) by leveraging Tool Centre Point (TCP) position and orientation data, along with joints{\textquoteright} currents measurements of cobots, to detect anomalies caused by functional deviations. The proposed framework employs multiple Long Short-Term Memory AutoEncoder (LSTM-AE) models, and the results obtained demonstrate that the framework is highly effective in detecting and isolating cobot anomalies, with high potential to be developed into a smart solution for identifying real faults.",

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author = "Samuel Ayankoso and Yang Guan and Fengshou Gu and Hassna Louadah and Hamidreza Fahham and Andrew Ball",

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Ayankoso, S, Guan, Y, Gu, F, Louadah, H, Fahham, H & Ball, A 2025, Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots. in M Singh, G Soni, J Sinha, AD Ball, F Gu, H Ouyang & C Featherston (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024—Volume 1. 1 edn, Mechanisms and Machine Science, vol. 181 MMS, Springer, Cham, pp. 723-738, UNIfied International Conference on Emerging Technologies in Cyber-Physical Systems and Industrial AI, Jaipur, India, 26/11/24. https://doi.org/10.1007/978-3-031-93327-1_45

Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots. / Ayankoso, Samuel; Guan, Yang; Gu, Fengshou et al.
Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024—Volume 1. ed. / Maneesh Singh; Gunjan Soni; Jyoti Sinha; Andrew D. Ball; Fengshou Gu; Huajiang Ouyang; Carol Featherston. 1. ed. Springer, Cham, 2025. p. 723-738 (Mechanisms and Machine Science; Vol. 181 MMS).

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

TY - GEN

T1 - Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots

AU - Ayankoso, Samuel

AU - Guan, Yang

AU - Gu, Fengshou

AU - Louadah, Hassna

AU - Fahham, Hamidreza

AU - Ball, Andrew

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2025/10/4

Y1 - 2025/10/4

N2 - Amidst the rapidly advancing landscape of robotics, AI, and IoT, collaborative robots (cobots) play a pivotal role in the transition to Industry 5.0. The precise positioning and performance of cobots are crucial for ensuring product quality and worker safety. This paper examines system and subsystem-level Prognostics and Health Management (PHM) by leveraging Tool Centre Point (TCP) position and orientation data, along with joints’ currents measurements of cobots, to detect anomalies caused by functional deviations. The proposed framework employs multiple Long Short-Term Memory AutoEncoder (LSTM-AE) models, and the results obtained demonstrate that the framework is highly effective in detecting and isolating cobot anomalies, with high potential to be developed into a smart solution for identifying real faults.

AB - Amidst the rapidly advancing landscape of robotics, AI, and IoT, collaborative robots (cobots) play a pivotal role in the transition to Industry 5.0. The precise positioning and performance of cobots are crucial for ensuring product quality and worker safety. This paper examines system and subsystem-level Prognostics and Health Management (PHM) by leveraging Tool Centre Point (TCP) position and orientation data, along with joints’ currents measurements of cobots, to detect anomalies caused by functional deviations. The proposed framework employs multiple Long Short-Term Memory AutoEncoder (LSTM-AE) models, and the results obtained demonstrate that the framework is highly effective in detecting and isolating cobot anomalies, with high potential to be developed into a smart solution for identifying real faults.

KW - Anomalies

KW - Cobots

KW - Health assessment

KW - Industry 5.0

KW - LSTM autoencoder

KW - Prognostics and health management

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UR - https://doi.org/10.1007/978-3-031-93327-1

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DO - 10.1007/978-3-031-93327-1_45

M3 - Conference contribution

AN - SCOPUS:105020007352

SN - 9783031933264

SN - 9783031933295

T3 - Mechanisms and Machine Science

SP - 723

EP - 738

BT - Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences

A2 - Singh, Maneesh

A2 - Soni, Gunjan

A2 - Sinha, Jyoti

A2 - Ball, Andrew D.

A2 - Gu, Fengshou

A2 - Ouyang, Huajiang

A2 - Featherston, Carol

PB - Springer, Cham

T2 - UNIfied International Conference on Emerging Technologies in Cyber-Physical Systems and Industrial AI

Y2 - 26 November 2024 through 28 November 2024

ER -

Ayankoso S, Guan Y, Gu F, Louadah H, Fahham H , Ball A. Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots. In Singh M, Soni G, Sinha J, Ball AD, Gu F, Ouyang H, Featherston C, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024—Volume 1. 1 ed. Springer, Cham. 2025. p. 723-738. (Mechanisms and Machine Science). Epub 2025 Oct 3. doi: 10.1007/978-3-031-93327-1_45

Multi-Model-Based Condition Monitoring Framework for the Health Assessment of the Tool Center Point and Joints of Industrial Collaborative Robots

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