Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations

Moncef Soualhi (), Khanh T. P. Nguyen (), Kamal Medjaher (), Denis Lebel () and David Cazaban ()
Additional contact information
Moncef Soualhi: Université de Bourgogne Franche-Comté CNRS/UFC/ENSMM
Khanh T. P. Nguyen: Toulouse University INPT-ENIT
Kamal Medjaher: Toulouse University INPT-ENIT
Denis Lebel: Technology Transfer Center, METALLICADOUR
David Cazaban: Technology Transfer Center, METALLICADOUR

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 4, No 13, 1743-1759

Abstract: Abstract In the age of Industry 4.0, multi-axis robots are widely used in smart manufacturing thanks to their capacity of milling high complex forms and interacting with several systems in production lines. However, during manufacturing, the occurrence of small drifts in the robot arms may lead to critical failures and significant product quality damages and, therefore, high financial losses. Hence, this paper aims to develop an effective and practical methodology for online diagnostics of robot drifts based on information fusion of direct and indirect monitoring. The direct monitoring exploits the already installed encoders on each servomotor of the robot while the indirect monitoring uses heterogeneous sensors (current, vibration, force and torque) placed at the robot tool level. The sensor measurements of the robot tool are processed, in an offline phase, to build health indicators and fused to learn a classifier for drifts detection and diagnostics. Then, during the online phase and in the case of presence of new drift patterns, the encoder measurements are used to label these patterns and update the classifier learned previously to diagnose their origin. The efficiency and robustness of the proposed methodology are verified through a real industrial machining multi-axis robot that investigates different drift severities of its arms.

Keywords: Prognostics and health management; Condition monitoring; Intelligent monitoring; Fault detection and diagnostics; Signal processing; Information fusion; Multi-axis robot; Industry 4.0; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-021-01882-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01882-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-021-01882-0

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().