Online data-driven fault detection for robotic systems

In this paper we demonstrate the online applicability of the fault detection and diagnosis approach which we previously developed and published in [1]. In our former work we showed that a purely data driven fault detection approach can be successfully built based on monitored inter-component communication data of a robotic system and used for a-posteriori fault detection. Here we propose an extension to this approach which is capable of online learning of the fault model as well as for online fault detection. We evaluate the application of our approach in the context of a RoboCup task executed by our service robot BIRON in corporation with an expert user.