N
Nan Chen
Researcher at National University of Singapore
Publications - 80
Citations - 3443
Nan Chen is an academic researcher from National University of Singapore. The author has contributed to research in topics: Statistical process control & Control chart. The author has an hindex of 24, co-authored 73 publications receiving 2235 citations. Previous affiliations of Nan Chen include University of Wisconsin-Madison.
Papers
More filters
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.
Journal ArticleDOI
The Inverse Gaussian Process as a Degradation Model
Zhi-Sheng Ye,Nan Chen +1 more
TL;DR: In this article, the inverse Gaussian process (IG) is used as a limiting compound Poisson process to model degradation of products deteriorating in random environments, which makes the IG process much more attractive compared with the Gamma process, which has been thoroughly investigated in the literature of degradation modeling.
Journal ArticleDOI
Prognostics and Health Management: A Review on Data Driven Approaches
TL;DR: This paper provides a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches.
Journal ArticleDOI
A new class of Wiener process models for degradation analysis
Zhi-Sheng Ye,Nan Chen,Yan Shen +2 more
TL;DR: A new class of random effects model for the Wiener process model is proposed and one of the parameters is allowed to be random across the product population so that a unit with a high degradation rate would also possess high volatility.
Journal ArticleDOI
Condition-based maintenance using the inverse Gaussian degradation model
TL;DR: It is proved that the monotone control limit policy is optimal and sensitivity analysis of the model parameters on the optimal policy is conducted.