Data-driven models for fault classification and prediction of industrial robots

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.