Mechanical Fault Diagnosis of a DC Motor Utilizing United Variational Mode Decomposition, SampEn, and Random Forest-SPRINT Algorithm Classifiers

Traditional fault diagnosis methods of DC (direct current) motors require establishing accurate mathematical models, effective state and parameter estimations, and appropriate statistical decision-making methods. However, these preconditions considerably limit traditional motor fault diagnosis metho...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Zijian, Liu, Mingliang, Qin, Huabin, Li, Bing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514959/
https://www.ncbi.nlm.nih.gov/pubmed/33267184
http://dx.doi.org/10.3390/e21050470

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514959/
https://www.ncbi.nlm.nih.gov/pubmed/33267184
http://dx.doi.org/10.3390/e21050470

Ejemplares similares