A combined deep learning model for damage size estimation of rolling bearing

Abstract

The identification of damage sizes based on vibration monitoring data is significant in the study of fault evolution, prediction and diagnosis of rolling bearing. There are inherent restrictions in traditional identification models such as high dependence on prior knowledge and insufficient feature extraction, aiming at the limitation, a prediction method of rolling bearing damage size based on deep learning is proposed. In this paper, a combined model of deep convolutional long-short-term memory network is developed, which can sufficiently extract the multi-dimensional and time-series characteristics of bearing vibration signal, and realize the intelligent and efficient diagnosis of bearing fault. On the basis of theoretical analysis, the rolling bearing fault tests under various damage sizes and rotational velocities are carried out by using the accelerated fatigue testing machine for rolling bearings, and the traditional and novel methods are compared based on the test data. The results show that the prediction accuracy of the combined network can reach 99.94% and 98.67%, respectively under normal and noisy conditions, which is higher than the single deep convolution network, long-short-term memory network and other models. The comparison results amply demonstrate the superiority of the proposed method.