ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper,...

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Bibliographic Details
Published in:Applied sciences 2023-01, Vol.13 (1), p.143
Main Authors: Nabissi, Giacomo, Longhi, Sauro, Bonci, Andrea
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Case studies
Collaboration
collaborative robots
Complex systems
condition monitoring
Environmental conditions
Fault detection
Flexible manufacturing systems
Manipulators
Manufacturing
Robotic Operating System (ROS)
Robots
Sensors
System effectiveness
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Summary:The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13010143