Bearing fault diagnosis with kernel sparse representation classification based on adaptive local iterative filtering-enhanced multiscale entropy features

DOI10.1155/2019/7905674zbMath1435.62441OpenAlexW2949036315WikidataQ127757528 ScholiaQ127757528MaRDI QIDQ2298789

Yanyan Li

Publication date: 20 February 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2019/7905674

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30)

Uses Software

• CVX

Cites Work

• Bearing fault diagnosis based on multiscale permutation entropy and support vector machine
• Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis
• Approximate entropy as a measure of system complexity.
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Variational Mode Decomposition

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