2026-09-16T01:38:53Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/83582024-01-17T10:55:19Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

00925njm 22002777a 4500 dc Basurto Hornillos, Nuño author Cambra Baseca, Carlos author Herrero Cosío, Álvaro author 2021 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. 0925-2312 http://hdl.handle.net/10259/8358 10.1016/j.neucom.2020.05.101 1872-8286 Component-based robot Missing values Data balancing Anomaly detection Supervised learning Support Vector Machine Improving the detection of robot anomalies by handling data irregularities