Xiang Li
Zhejiang Sci-Tech University
10 Papers
Xiang Li is an academic researcher from Zhejiang Sci-Tech University. The author has contributed to research in topics: Computer science & Nonparametric statistics. The author has an hindex of 1, co-authored 1 publications.
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Papers
Investigation of the Correlations between Amino Acids, Amino Acid Mixtures and Dipeptides by Terahertz Spectroscopy
TL;DR: Solid evidences confirm that THz spectroscopy can be availably used to analyze the correlations of amino acids, amino acid mixtures, and dipeptides, as well as to identify dipepeptide and its isomer from molecular level.
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A graph neural network-based data cleaning method to prevent intelligent fault diagnosis from data contamination
Shuhui Wang,Yaguo Lei,Bin Yang,Xiang Li,Yue Shu,Na Lu +5 more
TL;DR: A graph neural network-based method is proposed to prevent data contamination in mechanical fault diagnosis, detecting anomalies and correcting mislabeled data through graph clustering, improving DL model performance in fault diagnosis tasks.
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Rigid-flexible coupled modeling of compound multistage gear system considering flexibility of shaft and gear elastic deformation
Xiao Yang,Yaguo Lei,Huan Liu,Bin Yang,Xiang Li,Naipeng Li +5 more
TL;DR: A rigid-flexible coupled model is proposed for compound multistage gear systems, considering gear elastic deformation and shaft flexibility, to analyze dynamic behavior, load-dependent time-varying mesh stiffness, and coupled interactions among subsystems.
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A new nonparametric degradation modeling method for truncated degradation signals by axis rotation
TL;DR: In this article , a degradation model based on the FPCA by axis rotation is proposed, where the degradation signals of different units are truncated at the same failure threshold level, and the axis rotation strategy ensures the same scale in X-axis.
11
A nonparametric degradation modeling method for remaining useful life prediction with fragment data
Naipeng Li,Mingyang Wang,Yaguo Lei,Xiaosheng Si,Bin Yang,Xiang Li +5 more
- 01 May 2024
TL;DR: This paper proposes a nonparametric degradation modeling method for remaining useful life prediction with fragment data, using a state-dependent framework and functional principal component analysis to recover missing data and predict RUL in industrial equipment.
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