Funder
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
Technological Innovation Projects of Shandong Province
Publisher
Springer Science and Business Media LLC
Reference50 articles.
1. Al-Dulaimi, A., Zabihi, S., Asif, A., & Mohammadi, A. (2019). A multimodal and hybrid deep neural network model for remaining useful life estimation. Computers in Industry, 108, 186–196.
2. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
3. Chen, Z., Chen, B., & Chen, X. (2022). Remaining useful life prediction of turbofan engine based on temporal convolutional networks optimized by genetic algorithm. In: Journal of Physics: Conference Series, vol. 2181, p. 012001. IOP Publishing
4. Chen, J., Chen, D., & Liu, G. (2021). Using temporal convolution network for remaining useful lifetime prediction. Engineering Reports, 3(3), 12305.
5. Chen, Y., Zhang, D., & Zhang, W. A. (2022). Mswr-lrcn: A new deep learning approach to remaining useful life estimation of bearings. Control Engineering Practice, 118, 104969.