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Data-Driven Robot Fault Detection and Diagnosis Using Generative Models: A Modified SFDD Algorithm

  • 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.

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Document Type:Conference Object
Author:Alex MitrevskiORCiD, Paul G. Plöger
Parent Title (English):30th International Workshop on Principles of Diagnosis DX'19, November 11-13, 2019, Klagenfurt, Austria
Number of pages:8
Date of first publication:2019/10/08
Publication status:accepted
Funding Information:ROPOD is an Innovation Action funded by the European Commission under grant no. 731848 within the Horizon 2020 framework program.
Keyword:anomaly detection; learning-based fault detection and diagnosis; robotics; sensor-based fault detection and diagnosis
Departments, institutes and facilities:Fachbereich Informatik
Projects:ROPOD - Ultra-flat, ultra-flexible, cost-effective robotic pods for handling legacy in logistics (EC/H2020/731848)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2019/10/04