Future Blog Post
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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Beamer
Published:
A tutorial for conda for Beamer
OneDrive
Published:
解决“缓存凭据已到期”问题
Ablation Study
Published:
在知乎上看到一个关于消融实验的通俗易懂的解释
LaTeX
Published:
A tutorial for LaTeX used in VScode
Colab
Published:
A tutorial for colab
Markdown
Published:
Conda
Published:
A tutorial for conda
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
publications
Multi-sensor data-driven remaining useful life prediction of semi-observable systems
Published in IEEE Transactions on Industrial Electronics, 2020
Recommended citation: Li, N., Lei, Y., Gebraeel, N., Wang, Z., Cai, X., Xu, P., & Wang, B. (2020). Multi-sensor data-driven remaining useful life prediction of semi-observable systems. IEEE Transactions on Industrial Electronics, 68(11), 11482-11491.
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Remaining useful life prediction based on a multi-sensor data fusion model
Published in Reliability Engineering & System Safety, 2021
Recommended citation: Li, N., Gebraeel, N., Lei, Y., Fang, X., Cai, X., & Yan, T. (2021). Remaining useful life prediction based on a multi-sensor data fusion model. Reliability Engineering & System Safety, 208, 107249.
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A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds
Published in Mechanical Systems and Signal Processing, 2022
Recommended citation: Li, N., Xu, P., Lei, Y., Cai, X., & Kong, D. (2022). A self-data-driven method for remaining useful life prediction of wind turbines considering continuously varying speeds. Mechanical Systems and Signal Processing, 165, 108315.
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RUL prediction for two-phase degrading systems considering physical damage observations
Published in Reliability Engineering & System Safety, 2024
Recommended citation: Cai, X., Li, N., & Xie, M. (2024). RUL prediction for two-phase degrading systems considering physical damage observations. Reliability Engineering & System Safety, 244, 109926.
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A self-data-driven approach for online remaining useful life prediction of machinery using a recursive update strategy
Published in Mechanical Systems and Signal Processing, 2025
Recommended citation: Xu, P., Lei, Y., Wang, Z., Li, N., Cai, X., & Feng, K. (2025). A self-data-driven approach for online remaining useful life prediction of machinery using a recursive update strategy. Mechanical Systems and Signal Processing, doi: https://doi.org/10.1016/j.ymssp.2025.112541.
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Knowledge embedded spatial-temporal graph convolutional networks for remaining useful life prediction
Published in Reliability Engineering & System Safety, 2025
Recommended citation: Cai, X., Zhang, D., Yu, Y., & Xie, M. (2025). Knowledge embedded spatial-temporal graph convolutional networks for remaining useful life prediction. Reliability Engineering & System Safety, doi: https://doi.org/10.1016/j.ress.2025.110928.
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Small-sample prediction validation testing: uncertainty-aware design and robust maintenance strategy for power electronic converters
Published in IEEE Transactions on Power Electronics, 2025
Recommended citation: Sun, Q., Chen, C., Cai, X., Gao, J., Ye, X., Zhai, G., & Xie, M. (2025). Small-sample prediction validation testing: Uncertainty-aware design and robust maintenance strategy for power electronic converters. IEEE Transactions on Power Electronics, doi: https://doi.org/10.1109/TPEL.2025.3587316.
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