Remaining Useful Life Prediction Method for Rolling Bearings Based on CBAM-CNN-BiLSTM

Rolling bearings, as a rotating component, are of great importance to ensure the normal operation and smooth running of important equipment. Remaining useful life (RUL) prediction is a hot research topic in the engineering field, which is helpful to ensure the operational safety of system equipment and reduce maintenance cost. The topic of how to utilize the important feature information in the time-series data and the reasonable use of attention mechanism are addressed in this study with a CBAM-CNN-BiLSTM-based technique for estimating the remaining service life of rolling bearings. Firstly, multi-domain features of vibration signals are extracted from time domain, frequency domain and time-frequency domain, and the features are normalized to the maximum-minimum value. Then, a convolutional neural network incorporating a hybrid convolutional attention module is used to extract the important features; a bidirectional long- and short-term memory network is employed to obtain the before-and-after dependencies in the features. Next, the self-attention mechanism is introduced into the bidirectional long and short-term network to focus on more important deep features. Finally, the effectiveness of the method is verified by the XJTU-SY dataset. The comparative study shows that the proposed CBAM-CNN-BiLSTM model outperforms other state-of-the-art methods in RUL prediction and system prediction, with higher prediction accuracy and generalization performance.