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.
