基于时频域信号优化器的Mi-MkTCN轴承寿命预测模型OA

Rolling bearings were recognized as common key components in mechanical equipment.Accurate prediction of their re-maining service life was considered crucial for safe and stable operation.A Multi inflated Multi kernel Time Convolutional Net-work(Mi-MkTCN)model was proposed to address current challenges in bearing life prediction.The model was based on a Time-Frequency domain signal Ratio Optimizer(TFRO).Three main problems were targeted:unclear bearing degradation characteris-tics,poor model generalization ability,and difficulty in capturing long-term data dependencies.The TFRO optimizer was designed to accurately retain important information.At each time node,past and current information were reassembled.Important time-fre-quency domain features from past information were proportionally allocated.Multiple dilation methods were employed in Mi-MkTCN to prevent loss of important features.A multi-kernel temporal convolutional network was then used to extract features at different scales.The effectiveness of the proposed model improvement method was demonstrated through ablation experiments.Al-gorithm comparison studies were conducted to verify the superiority of the TFRO-based Mi-MkTCN model.Performance metrics were recorded as follows:MAE(0.001 45),MSE(0.050 69),and RMSE(0.120 45).The experimental results showed that the proposed method significantly improved the prediction accuracy of the remaining service life of bearings,providing a high-precision and highly robust solution for predicting the remaining service life of bearings.