RT info:eu-repo/semantics/article
T1 Improving the detection of robot anomalies by handling data irregularities
A1 Basurto Hornillos, Nuño
A1 Cambra Baseca, Carlos
A1 Herrero Cosío, Álvaro
K1 Component-based robot
K1 Missing values
K1 Data balancing
K1 Anomaly detection
K1 Supervised learning
K1 Support Vector Machine
K1 Informática
K1 Computer science
AB The ever-increasing complexity of robots causes failures of them as a side effect. Successful detection of anomalies in robotic systems is a key issue in order to improve their maintenance and consequently reducing economic costs and downtime. Going one step further in the detection of anomalies in robots, different mechanisms to deal with data irregularities are proposed and validated in present paper in order to increase detection rates. More precisely, strategies to overcome missing values and class imbalance are considered as complementary tools to get better one-class classification results. The effect of such strategies is evaluated through cross-validation when applying a standard supervised learning model, the Support Vector Machine. Experiments are run on an up-to-date and public dataset that contains some examples of different software anomalies that the middleware of the robot under analysis may experience.
PB Elsevier
SN 0925-2312
YR 2021
FD 2021
LK http://hdl.handle.net/10259/8358
UL http://hdl.handle.net/10259/8358
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 11-may-2024