Rolling bearing performance degradation assessment based on improved convolutional neural network with anti-interference

Abstract

Aiming at the difficulty in evaluating and identifying the degradation performance of rolling bearing, an intelligent approach based on deep autoencoder (DAE), t-distribution stochastic neighbor embedding (t-SNE) and the improved convolutional neural network (CNN) is proposed in this research. Firstly, the characteristics about the performance degradation of bearing signal are extracted, expressed and reduced by the DAE and the t-SNE model. Subsequently, the Mahalanobis distance (MD) in the low-dimensional feature space is constructed as an indicator for reflecting the bearing performance degradation. After that, the CNN is trained based on the tagged bearing data. In order to suppress the over-fitting of the model, training samples are added with noise. Moreover, Leaky ReLU (LReLU) function and dropout are used as the activation function to improve the anti-interference ability. Finally, the results of performance degradation assessment show that the proposed method has more accurate performance than some existing methods.