Cargando…
Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback–Leibler Divergence with a Gaussian Mixture Model
Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose d...
Autores principales: | Huang, Yi-Cheng, Hou, Ching-Chen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347128/ https://www.ncbi.nlm.nih.gov/pubmed/37448023 http://dx.doi.org/10.3390/s23136174 |
Ejemplares similares
-
Kullback–Leibler divergence and the Pareto–Exponential approximation
por: Weinberg, G. V.
Publicado: (2016) -
Computation of Kullback–Leibler Divergence in Bayesian Networks
por: Moral, Serafín, et al.
Publicado: (2021) -
Kullback Leibler divergence in complete bacterial and phage genomes
por: Akhter, Sajia, et al.
Publicado: (2017) -
Kullback–Leibler Divergence of a Freely Cooling Granular Gas
por: Megías, Alberto, et al.
Publicado: (2020) -
A data assimilation framework that uses the Kullback-Leibler divergence
por: Pimentel, Sam, et al.
Publicado: (2021)