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Weiwen Peng
Researcher at University of Electronic Science and Technology of China
Publications - 65
Citations - 2757
Weiwen Peng is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Reliability (statistics) & Computer science. The author has an hindex of 20, co-authored 54 publications receiving 1758 citations. Previous affiliations of Weiwen Peng include Sun Yat-sen University & National University of Singapore.
Papers
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Journal ArticleDOI
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network
Jun Zhu,Nan Chen,Weiwen Peng +2 more
TL;DR: A new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN) is presented, which shows enhanced performance in the prediction accuracy.
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Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty
TL;DR: A comprehensive uncertainty quantification procedure is presented to quantify multiple types of uncertainty using multiplicative and additive UQ methods and the factors that contribute the most to the resulting output uncertainty are investigated and identified for uncertainty reduction in decision-making.
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Computational-experimental approaches for fatigue reliability assessment of turbine bladed disks
TL;DR: Results indicate that stochastic FE analysis-based scheme provides more conservative predictions than the probabilistic S-N curves-based one.
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Reliability analysis of complex multi-state system with common cause failure based on evidential networks
TL;DR: The reliability analysis of servo feeding control system for CNC heavy-duty horizontal lathes (HDHLs) by this proposed method has shown that CCFs have considerable impact on system reliability.
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Inverse Gaussian process models for degradation analysis: A Bayesian perspective
TL;DR: A Bayesian analysis of inverse Gaussian process models for degradation modeling and inference and a classic example is presented to demonstrate the applicability of the Bayesian method for degradation analysis with the inverse Gaussia process models.