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Robot fault detection and remaining life estimation for predictive maintenance

Riccardo Pinto, Tania Cerquitelli

Year
2019
Citations
47

Abstract

Abstract In this work some possible solutions to implement a Robotics-oriented predictive maintenance approach are discussed. The data-driven methodology is described from the data collection to the design of an appropriate dataset and finally to the use of some of the most promising algorithms in the field of machine learning. The whole process is composed by several building blocks that can be combined to realize a data analysis on industrial robots. Some of the most promising techniques in Predictive Maintenance for Industrial machines were included in the proposed methodology, together with a Survival Analysis study, and then evaluated with proper performance metrics. Experimenting this methodology on a real use-case with Comau industrial robots showed the validity of the approach and opened to the inclusion of such a process in a service-oriented solution.

Keywords

Computer sciencePredictive maintenanceRobotRoboticsField (mathematics)Process (computing)Fault detection and isolationArtificial intelligenceMachine learningReliability engineering

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