Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include linear model, neural networks model, 1-nearest neighbor and random forest model. For every kind of model, a variable selection method should be used. In our thesis we try to find the best model for this problem, and also select the most important input signals. After we compare these four models, we found that the best accuracy (96.9% correct classifications) was achieved with the random forest model.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different supervised methods and achieving 54% of accuracy. An approach related to tree-structure of classifiers obtained better results in comparison with a hierarchical approach. / Revisión por pares