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Article

Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning

by and *,†
Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10138 Torino, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Emanuele Carpanzano
Appl. Sci. 2021, 11(4), 1621; https://doi.org/10.3390/app11041621
Received: 11 January 2021 / Revised: 29 January 2021 / Accepted: 4 February 2021 / Published: 10 February 2021
(This article belongs to the Special Issue Systems Engineering: Availability and Reliability)
Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors. View Full-Text
Keywords: on-line monitoring; collaborative robots; human robot collaboration; machine learning on-line monitoring; collaborative robots; human robot collaboration; machine learning
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MDPI and ACS Style

Aliev, K.; Antonelli, D. Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Appl. Sci. 2021, 11, 1621. https://doi.org/10.3390/app11041621

AMA Style

Aliev K, Antonelli D. Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning. Applied Sciences. 2021; 11(4):1621. https://doi.org/10.3390/app11041621

Chicago/Turabian Style

Aliev, Khurshid, and Dario Antonelli. 2021. "Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning" Applied Sciences 11, no. 4: 1621. https://doi.org/10.3390/app11041621

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