Featured Application: This work aimed to enhance the current state of the art regarding the Condition Monitoring of industrial collaborative manipulators providing a general architecture which can be used in many different industrial scenarios. This solution is helpful in overcoming current limits regarding the definition of algorithms for the automatic detection of failures in collaborative robots working on flexible manufacturing. 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.
ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots / Nabissi, G.; Longhi, S.; Bonci, A.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - ELETTRONICO. - 13:1(2022), pp. 1-22. [10.3390/app13010143]