TY - CHAP U1 - Konferenzveröffentlichung A1 - Mitrevski, Alex A1 - Plöger, Paul G. T1 - Data-Driven Robot Fault Detection and Diagnosis Using Generative Models: A Modified SFDD Algorithm T2 - 30th International Workshop on Principles of Diagnosis DX'19, November 11-13, 2019, Klagenfurt, Austria N2 - 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. KW - learning-based fault detection and diagnosis KW - sensor-based fault detection and diagnosis KW - anomaly detection KW - robotics SP - 8 S1 - 8 ER -