- B. Wang, Y. Lei, T. Yan, N. Li, and L. Guo, “Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery,” Neurocomputing, vol. X, pp. X-X, 2019. (IF: 4.072)
B. Wang, Y. Lei, N. Li, and T. Yan, “Deep separable convolutional network for remaining useful life prediction of machinery,” Mechanical Systems and Signal Processing, vol. 134, pp. 1-18, 2019. (IF: 5.005)
B. Wang, T. Han, Y. Lei, and N. Li, “Remaining Useful Life Prediction Based on Deep Residual Attention Network,” presented at the 2019 International Conference on Sensing, Diagnostics, Prognostics and Control, Beijing, China, August 15-17, 2019.
雷亚国, 韩天宇, 王彪, 李乃鹏, 闫涛, and 杨军, “XJTU-SY滚动轴承加速寿命试验数据集解读,” 机 械 工 程 学 报, vol. 55, no. 16, pp. 1-6, 2019.
- B. Wang, Y. Lei, N. Li, and N. Li, “A hybrid prognostics approach for estimating remaining useful life of rolling element bearings,” IEEE Transactions on Reliability, pp. 1-12, 2018. (IF: 2.888)
- B. Wang, Y. Lei, N. Li, and J. Lin, “An improved fusion prognostics method for remaining useful life prediction of bearings,” presented at the 2017 IEEE International Conference on Prognostics and Health Management, Dallas, TX, USA, June 19-21, 2017.