Remaining Useful Life Prediction of Bearings Using Reverse Attention Graph Convolution Network with Residual Convolution Transformer

Bearings are key components in mechanical equipment and remaining useful life (RUL) prediction of bearings is of great significance for stable production in industry. To predict RUL accurately, the time-frequency diagram and the long-term dependence of vibration signals have been widely researched. However, the linear frequency scale of time-frequency diagram is unfavorable to balance characteristic defect of low-frequency and natural vibration of high-frequency. Also, abrupt change vibration cause serious interference to long-term dependence. Therefore, based on theoretical analysis of bearings vibration, we introduce Mel-scale frequency cepstral coefficients (MFCC) three-channels image and propose reverse attention graph convolution network with residual convolution transformer (RCT-RAGCN). MFCC three-channels image optimizes the frequency region by logarithmic Mel scale. Reverse attention improves aggregation method of long-term dependence and alleviates the interference of abrupt change, approaches long-term dependency problems from signal difference perspective. In constructed graphs, reverse attention matrices are as edges, which is profit for aggregation of abrupt change by graph convolution network (GCN). In proposed encoder, residual convolution extract features from MFCC three-channels image and Transformer learn raw signals to prevent information loss caused in constructing MFCC. On the IEEE PHM 2012 dataset, results indicate that the proposed method outperforms five advanced models in term of accuracy. The ablation studies verify the significant role of proposed method.