Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

DOI10.1142/S2424922X17500048zbMath1369.94271MaRDI QIDQ4977888

James R. Ottewill, Pawel Rzeszucinski, Michal Juraszek

Publication date: 17 August 2017

Published in: Advances in Data Science and Adaptive Analysis (Search for Journal in Brave)

zbMATH Keywords

robustnessform factordiagnosticsgearboxensemble empirical mode decompositionfinding the optimal parametersHilbert-Huang spectrumsparsity measurementsparsity measurement (SM)time synchronous averaging (TSA)

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• Foundations of time-frequency analysis
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Comparing Measures of Sparsity
• A Robust Fuzzy Local Information C-Means Clustering Algorithm

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