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.
| Document Type: | Conference Object |
|---|---|
| Language: | English |
| 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 |
