Junxian Shen
Harbin Engineering University
27 Papers
Junxian Shen is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 3, co-authored 5 publications.
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Papers
An integrated method based on hybrid grey wolf optimizer improved variational mode decomposition and deep neural network for fault diagnosis of rolling bearing
TL;DR: The improved VMD method after parameter optimization can extract the early failure characteristics of rolling bearing more distinctly, and the fault diagnosis model based on this method has higher accuracy and application value.
145
A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox
TL;DR: The experimental results show that this method could effectively improve the self-adaptive feature extraction ability of the model as well as its accuracy of fault diagnosis, which has better generalization performance.
Method of Fault Feature Selection and Fusion Based on Poll Mode and Optimized Weighted KPCA for Bearings
Junxian Shen,Fei Yun Wu +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a feature selection and fusion method based on poll mode and optimized Weighted Kernel Principal Component Analysis (WKPCA) method, which can improve the separability in the subset of fault samples effectively.
28
Blade crack detection using variational model decomposition and time-delayed feedback nonlinear tri-stable stochastic resonance
TL;DR: In this paper , a variational mode decomposition (VMD) and time-delayed feedback nonlinear tri-stable stochastic resonance (TFNTSR) method was proposed to solve the problem of blade crack detection.
11
A Bearing Performance Degradation Modeling Method Based on EMD-SVD and Fuzzy Neural Network
Jingbo Gai,Yifan Hu,Junxian Shen +2 more
TL;DR: A novel degradation modeling method based on EMD-SVD and fuzzy neural network was proposed to identify and evaluate the degradation process of bearings in the whole life cycle accurately and provide an important maintenance strategy for the CBM of bearings.