  This paper presents a modification of the data-driven sensor-based fault
detection and diagnosis (SFDD) algorithm for online robot monitoring. Our
version of the algorithm uses a collection of generative models, in particular
restricted Boltzmann machines, each of which represents the distribution of
sliding window correlations between a pair of correlated measurements. We use
such models in a residual generation scheme, where high residuals generate
conflict sets that are then used in a subsequent diagnosis step. As a proof of
concept, the framework is evaluated on a mobile logistics robot for the problem
of recognising disconnected wheels, such that the evaluation demonstrates the
feasibility of the framework (on the faulty data set, the models obtained 88.6%
precision and 75.6% recall rates), but also shows that the monitoring results
are influenced by the choice of distribution model and the model parameters as
a whole.
