TY - GEN
T1 - A RUL Prediction Method of Motor Rolling Bearing Based on TCN-SA-BiLSTM Model
AU - Yang, Bowen
AU - Liu, Yuepeng
AU - Song, Kai
AU - Se, Haifeng
AU - Sun, Chuanyu
AU - Jiang, Jinhai
AU - Fan, Fulin
AU - Xue, Rui
N1 - Publisher Copyright:
© Beijing Paike Culture Commu. Co., Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - The traditional rolling bearing residual life prediction methods lack a clear learning mechanism and low model prediction accuracy, which cannot effectively extract the important degradation information features contained in the differences between different timing features. In order to further improve the accuracy of the prediction model, this paper proposes a prediction model of the temporal convolutional network (BiLSTM). Firstly, a multi-dimensional typical degradation feature set is built; Secondly, the time convolution network (TCN) integrating a self-attention mechanism to capture the dependence on various time scales, learn feature weights and improve the model’s attention to key features; finally, the BiLSTM time series prediction model is used to predict the bearing degradation trend and realize high-precision bearing life prediction. Using the data of seven motor bearings to evaluate the effectiveness, the results show that the TCN-SA-BiLSTM model showed excellent performance in the bearing life prediction task, which can significantly enhance the robustness of prediction.
AB - The traditional rolling bearing residual life prediction methods lack a clear learning mechanism and low model prediction accuracy, which cannot effectively extract the important degradation information features contained in the differences between different timing features. In order to further improve the accuracy of the prediction model, this paper proposes a prediction model of the temporal convolutional network (BiLSTM). Firstly, a multi-dimensional typical degradation feature set is built; Secondly, the time convolution network (TCN) integrating a self-attention mechanism to capture the dependence on various time scales, learn feature weights and improve the model’s attention to key features; finally, the BiLSTM time series prediction model is used to predict the bearing degradation trend and realize high-precision bearing life prediction. Using the data of seven motor bearings to evaluate the effectiveness, the results show that the TCN-SA-BiLSTM model showed excellent performance in the bearing life prediction task, which can significantly enhance the robustness of prediction.
KW - Bidirectional Length Network
KW - Remaining Useful Life
KW - Rolling Bearing
KW - Temporal Convolution Network
UR - https://www.scopus.com/pages/publications/105030826942
U2 - 10.1007/978-981-95-4294-9_1
DO - 10.1007/978-981-95-4294-9_1
M3 - 会议稿件
AN - SCOPUS:105030826942
SN - 9789819542932
T3 - Lecture Notes in Electrical Engineering
SP - 1
EP - 9
BT - The Proceedings of the 12th Frontier Academic Forum of Electrical Engineering, FAFEE 2025 - Volume VII
A2 - Yang, Qingxin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th Frontier Academic Forum of Electrical Engineering, FAFEE 2025
Y2 - 23 May 2025 through 25 May 2025
ER -