Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings

Abstract

Since the manual extraction of features is not sufficient to accurately characterize the health status of rolling bearings, machine learning algorithms are gradually being used for fault diagnosis of bearings, which can adaptively learn the required features from the input data. In this paper, $k$ -nearest neighbor, support vector machines, and convolutional neural networks are successfully applied to the fault diagnosis of bearings, for the benefit of achieving the detection and early warning of bearing fatigue damage. The original samples are segmented into semioverlapping samples. When using $k$ -nearest neighbor and support vector machines as early warning models, we searched their hyperparameters with random search and grid search, and the results showed that support vector machines could achieve 87.1% of bearing detection accuracy and $k$ -nearest neighbor could achieve 100% of detection accuracy. When convolutional neural networks are used as the early warning model, the accuracy can reach 99.75%.