EMD and GNN-adaboost fault diagnosis for urban rail train rolling bearings

DOI10.3934/DCDSS.2019101zbMath1419.90031OpenAlexW2901252779WikidataQ128862639 ScholiaQ128862639MaRDI QIDQ2321715

Guoqiang Cai, Jiaojiao Lv, Chen Yang, Yue Pan

Publication date: 23 August 2019

Published in: Discrete and Continuous Dynamical Systems. Series S (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/dcdss.2019101

zbMATH Keywords

fault diagnosisempirical mode decomposition (EMD)intrinsic mode functions (IMFs)rolling bearinggenetic neural network-adaptive boosting (GNN-Adaboost)

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Deterministic network models in operations research (90B10)

Uses Software

• AdaBoost.MH

Cites Work

• Unnamed Item
• A decision-theoretic generalization of on-line learning and an application to boosting
• Improved boosting algorithms using confidence-rated predictions
• The inapproximability for the (0,1)-additive number
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Constructing ID-based cryptographic technique for IFP and GDLP based cryptosystem
• Author's reply

This page was built for publication: EMD and GNN-adaboost fault diagnosis for urban rail train rolling bearings