Detection and Monitoring for Anomalies and Degradation of a Robotic Arm Using Machine Learning-Reference-Cited by-同舟云学术

Detection and Monitoring for Anomalies and Degradation of a Robotic Arm Using Machine Learning

Published:2021 Issue: Volume: Page:230-237
ISSN:2524-7247
Container-title:Advances in Automotive Production Technology – Theory and Application
language:
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Author:

Taha Hussein A.^ORCID,Yacout Soumaya,Birglen Lionel

Publisher

Springer Berlin Heidelberg

Link

https://link.springer.com/content/pdf/10.1007/978-3-662-62962-8_27

Reference12 articles.

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3. Goel, M., Maciejewski, A.A., Balakrishnan, V.: Analyzing unidentified locked-joint failures in kinematically redundant manipulators. J. Rob. Syst. 22(1), 15–29 (2005)

4. Qin, J., Léonard, F., Abba, G.: Real-time trajectory compensation in robotic friction stir welding using state estimators. IEEE Trans. Control Syst. Technol. 24(6), 2207–2214 (2016)

5. Qiao, G., Schlenoff, C., Weiss, B.A.: Quick positional health assessment for industrial robot prognostics and health management (PHM). In: 2017 IEEE International Conference on Robotics and Automation (ICRA) (2017)

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