Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings
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- Oscar Duque-Perez & Carlos Del Pozo-Gallego & Daniel Morinigo-Sotelo & Wagner Fontes Godoy, 2019. "Condition Monitoring of Bearing Faults Using the Stator Current and Shrinkage Methods," Energies, MDPI, vol. 12(17), pages 1-13, September.
- Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
- Li-Ching Wu & Hsin-Hao Chen & Jorng-Tzong Horng & Chen Lin & Norden E Huang & Yu-Che Cheng & Kuang-Fu Cheng, 2010. "A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-15, August.
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Keywords
condition-based monitoring (CbM); feature extraction; Hilbert-Huang transform (HHT); Hilbert marginal spectrum (HMS); k-Nearest Neighbour (kNN); machine learning (ML); mechanical bearings; prognostics; rotating machine; support vector machine (SVM); remaining useful life (RUL);All these keywords.
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