The ML-Pipeline uses stream of sensor data to analyze the behavior of automated objects in order to predict and diagnose the unknown faults and failures in an assembly process.
In the industrial robotics domain, faults have the potential to affect the efficiency of the underlying process, namely causing failures of internal physical components (e.g., robot, IPC, actuators), or even compromising the safety of humans interacting with the robot. When detecting a fault, usually a diagnosis process is induced in order to identify which internal components are involved. Enhancing failure detection by Machine Learning techniques will analyze real data and manipulated data streams in order to detect anomalies in the to be process. This will be achieved through process mining and pattern recognition in data from the original assembly process used to develop and train ML model. The resulting model will be used for failure detection in manipulated data streams in the selected test scenarios.

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