Rolling Bearing RUL Prediction Based on Fusion of Multi-Head Attention and Improved TCN-BiLSTM

Abstract

Rolling bearings are essential in the industrial field as a critical component of mechanical systems. Therefore, accurately predicting the remaining useful life of rolling bearings is vital to the safety and reliability of mechanical operation. However, traditional life prediction methods often have problems such as insufficient feature extraction and poor model generalization capabilities, which lead to more significant errors. To solve the above problems, this paper proposes a novel remaining useful life (RUL) prediction method of rolling bearings based on integrated multi-head attention (MHA), improved temporal convolutional network (TCN), and bidirectional long short-term memory (BiLSTM). This method utilizes an improved TCN-BiLSTM network to capture dependencies in sequences and extract global features from signals. In the meantime, MHA is introduced to fully capture the degradation information of the bearing and ultimately predict the life of the bearing. Finally, the bearing life prediction process is fully demonstrated through novel three-dimensional feature visualization. To verify the effectiveness of this method, this paper conducted RUL prediction experiments using the IEEE PHM 2012 dataset and the XJTU-SY dataset, respectively. Many experiments are organized to test the performance, and the experimental results show that this method has higher prediction accuracy and robustness than other methods.

Keywords

• Rolling bearing
• RUL prediction
• temporal convolutional network
• bidirectional long short-term memory
• multi-head attention