Name
Abstract | ||
|---|---|---|
The rise in complexity of robotic systems usually leads to an increase in failures of such systems. To improve the maintenance of this type of systems and thus reducing economic costs and downtime, present paper addresses anomaly detection in a component-based robot. To do so, the problem of anomaly detection is modelled as a classification problem, being Support Vector Machine (SVM) the selected classifier. It is applied to a publicly-available and recent dataset containing useful information about the performance of the software system in a component-based robot when certain anomalies are induced. Different preprocessing strategies and data sources are compared to get the best scores for some classification metrics through cross-validation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20055-8_23 | 14TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS (SOCO 2019) |
Keywords | Field | DocType |
Anomaly detection,Component-based robotic systems,Preprocessing,Missing values,Classification,Support vector machines | Anomaly detection,Computer science,Support vector machine,Software system,Preprocessor,Artificial intelligence,Missing data,Classifier (linguistics),Robot,Downtime,Machine learning | Conference |
Volume | ISSN | Citations |
950 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuño Basurto | 1 | 2 | 2.75 |
| ÁLvaro Herrero | 2 | 487 | 50.88 |