Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and low computational efficiency. In each iteration o...

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Detalles Bibliográficos
Autores principales: Hu, Lei, Wang, Ligui, Chen, Yanlu, Hu, Niaoqing, Jiang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460669/
https://www.ncbi.nlm.nih.gov/pubmed/36081069
http://dx.doi.org/10.3390/s22176599

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460669/
https://www.ncbi.nlm.nih.gov/pubmed/36081069
http://dx.doi.org/10.3390/s22176599

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