Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection

The subject of this research is the development of a classifier based on machine learning (ML) that is able to recognize defective and healthy ball bearings. For this purpose, vibration measurements were performed on the bearings, on a total of 196 samples. For each recorded vibration signal, a feature extraction was performed by digital processing in the time domain. The following ML algorithms were used to develop the classifier: K-nearest neighbor (KNN) and support vector machine (SVM) as well as improved versions of the aforementioned algorithms. Improved versions of the mentioned algorithms were obtained by optimizing their hyperparameters. The corresponding models of the KNN and SVM algorithms showed a high percentage of success in classification, 98.5 % and 99.5 %, respectively. By optimizing the hyperparameters, models with a maximum classification success of 100 % were achieved.