Temporal convolution long short-term memory network with multiple attention for remaining useful life prediction of rolling bearings

Remaining useful life (RUL) prediction of rolling bearings prevents sudden mechanical failures and reduces equipment maintenance costs. Due to the strong performance in time series forecasting tasks, the temporal convolutional network (TCN) has become a mainstream model for RUL prediction. However, existing TCN-based prediction models struggle to fully capture both long-term and global dependencies in complex data. To address these issues, a temporal convolutional long short-term memory network integrated with multi-head attention mechanism (TCLSTM-MA) is proposed to predict the rolling bearings' RUL. Firstly, the time-domain and frequency-domain features are extracted from the acquired raw vibration signals to form a complete degradation feature. Secondly, we enhance the traditional TCN by combining it with LSTM and introducing a multi-head attention mechanism. This integration allows the model to effectively capture both global degradation information and local context information. Additionally, a time-weighted t-MSE loss function is employed throughout training to make the model focus more on data close to failure points. Finally, the trained TCLSTM-MA model is used for RUL prediction. Extensive experiments were conducted on two authoritative rolling bearing datasets and compared with other methods. The experimental results demonstrate that the proposed method exhibits good accuracy and generalization capability.