@inproceedings{MitrevskiPloeger2019,
  author    = {Alex Mitrevski and Paul G. Pl{\"o}ger},
  title     = {Data-Driven Robot Fault Detection and Diagnosis Using Generative Models: A Modified SFDD Algorithm},
  series = {30th International Workshop on Principles of Diagnosis DX'19, November 11-13, 2019, Klagenfurt, Austria},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}