Remaining Useful Life Estimation for Rolling Bearings Using MSGCNN-TR

Abstract

Rolling bearing is one of the core parts of modern industrial equipment, with high safety and reliability. Therefore, predicting its remaining useful life (RUL) is essential and far-reaching. The critical difficulty of RUL estimation technology is how to extract the features reflecting the bearing degradation trend and how to select the RUL estimation algorithm. Although the RUL estimation algorithms based on deep learning [DL, such as convolutional neural network (CNN) and recurrent neural network (RNN)] have achieved remarkable results recently, these methods are not good at analyzing and predicting long time series. We propose a transformer prediction model with a multiscale gated CNN (MSGCNN-TR) to solve this question to predict the RUL of rolling bearings. This model mainly includes sample feature extraction and bearing RUL estimation. The first part is a feature extractor composed of a depth convolution path with residual and a gated recurrent unit (GRU) path. It can effectively extract the spatial and temporal features that reflect the bearing degradation trend in the rolling bearing life data. The second part is an RUL estimation model composed of a four-layer transformer encoder. The multihead self-attention mechanism in the transformer encoder can effectively capture the long- or short-term dependency relationship in the rolling bearing life data. Finally, we performed comparative and ablation experiments on the IEEE PHM2012 dataset. The results prove that the performance of the proposed model is better than those of other methods in the comparative experiment, and the ablation experiment proves the necessity and effectiveness of each module.