Xueliang Chen
5 Papers
1 Citations
Xueliang Chen is an academic researcher. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 1, co-authored 5 publications.
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Papers
Remaining Useful Life Prediction of Turbofan Engine Based on Temporal Convolutional Networks Optimized by Genetic Algorithm
Zhengkun Chen,Baojia Chen,Xueliang Chen +2 more
TL;DR: One RUL prediction model optimized by a genetic algorithm, based on temporal convolutional networks (TCN), was proposed, which shows that in the two evaluation metrics of root mean square error (RMSE) and score function (SF), the proposed GA-TCN reduces them by 8.2% and 28.24% when compared with other studies.
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A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed
Baojia Chen,Zhichao Hai,Xueliang Chen,Fafa Chen,Wenrong Xiao,Neng Qi Xiao,Wenlong Fu,Qiang Liu,Zhuxin Tian,Gongfa Li +9 more
TL;DR: Wang et al. as discussed by the authors proposed a time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed, which combined with the improved multisynchrosqueezing transform (IMSST), empirical Fourier decomposition (EFD), and generalized demodulation (GD).
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An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control
TL;DR: In this article, an extended state observer (ESO) based on the Takagi-Sugeno (T-S) fuzzy model is designed to estimate the system state and total disturbance.
Application of Feature Extraction Method Based on Empirical Fourier Decomposition in Rotor Rub-impact Fault
TL;DR: In this paper , a feature extraction technique using empirical Fourier decomposition (EFD) was proposed to reveal the time-frequency features of rotor rub-impact fault. And the Hilbert transform was used to estimate the instantaneous frequency (IF) and instantaneous amplitude (IA) of the signal generated by EFD.
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Research on Fault Feature Extraction of Rolling Bearing Based on dFIF
TL;DR: In this article , a rolling bearing feature recognition method based on direct fast iterative filtering (dFIF) and the crest factor of envelope spectrum is proposed, which can effectively realize rolling bearing fault detection.