Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system

DOI10.1080/00207721.2017.1397804zbMath1390.90273OpenAlexW2768705038MaRDI QIDQ4638020

Junxun Chen, Hui Yu, Shaolin Hu, Longsheng Cheng

Publication date: 3 May 2018

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2017.1397804

zbMATH Keywords

fault diagnosisintrinsic mode functionshealth assessmentensemble empirical mode decompositionhealth indexadjustment Mahalanobis-Taguchi system

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Fuzzy control/observation systems (93C42)

Cites Work

• Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach
• An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line
• A study on neuro-fuzzy systems for fault diagnosis
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Development of an Internet-based intelligent design support system for rolling element bearings

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