Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault

Bearings play a crucial role in machine longevity and is, at the same time, one of the most critical sources of failure in rotor dynamics. Particularly for journal bearings, it is not completely understood how specific damages may influence the response of the rotating system. Consequently, the identification of hydrodynamic bearing faults is challenging. Most of the literature relies on large amounts of training data collections from physical experiments or from the field, which are high in cost. This paper offers a deep learning approach to identify ovalization faults aiming to develop condition monitoring model-based strategies applied to hydrodynamic journal bearings. Therefore, a numerical model was developed to simulate the ovalization fault conditions in order to build training datasets. Afterwards, a deep convolutional neural network algorithm was trained with the generated datasets and used to predict the faults conditions. Finally, the identification performance was evaluated statistically regarding the true-positive identification by both probability density function and subjective logic. The classification accuracy showed promising results for training the machine learning algorithms with simulated data.