Data-driven Models for Fault Classification and Prediction of Industrial Robots
Corbinian Nentwich, Sebastian Junker, Günther Reinhart
Abstract
Economic data acquisition and storage have been key enablers to pave the way for data-driven predictions of machine downtimes. Regarding industrial robots, such predictions can maximize the robot’s availability and effective life span. This paper focuses on the comparison of different data-driven models for robot fault prediction and classification by applying them to a data set derived from a robot test bed and illuminates the data transformation process from raw sensor data to domain knowledge motivated robot health indicators.
Topics & Concepts
RobotRaw dataKey (lock)Process (computing)EngineeringData-drivenDomain (mathematical analysis)Data setSet (abstract data type)Fault (geology)Artificial intelligenceTest dataComputer scienceData miningSeismologyOperating systemGeologyProgramming languageMathematical analysisComputer securityMathematicsSoftware engineeringFault Detection and Control SystemsMineral Processing and GrindingMachine Fault Diagnosis Techniques