Speaker
Description
The research focuses on the classification of bearing damage using acoustic emission data. The methodology involves the collection of experimental data across six categories of bearing faults using three different types of sensors. Several neural network architectures are proposed and evaluated, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and InceptionTime. The study includes hyperparameter optimization and selection of a baseline model for performance comparison. To assess practical applicability, two key experiments are conducted: the first evaluating the impact of additional gearbox noise on classification accuracy, and the second testing the models’ ability to generalize across sensors not used during training.