DETECTION OF MACHINE FAILURES WITH MACHINE LEARNING METHODS

Abstract

This study aims to evaluate the effectiveness of classification algorithms such as Naive Bayes (NB), k-Nearest Neighbours (kNN) and Artificial Neural Networks (ANN) for machine fault detection and investigates the importance of feature selection. The dataset is analysed using cross-validation and the performance of the algorithms is evaluated in terms of AUC, accuracy, F1 Score, Precision and Sensitivity. Naive Bayes and ANN models have the highest AUC values and achieved 99.9% accuracy. kNN model has a lower AUC value than the others (76.0%), but has an accuracy of 97.2%. The feature selection analysis revealed that certain features such as HDF, OSF and PWF contribute significantly to the classification performance. These features have an important role to improve the effectiveness of classification algorithms in detecting faults. These results emphasize the effectiveness of algorithms and the importance of features in machine fault detection and contribute to the development of more reliable and efficient fault detection systems in industrial systems.