An improved wrapper-based feature selection method for machinery fault diagnosis

A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence th...

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Detalles Bibliográficos
Autores principales: Hui, Kar Hoou, Ooi, Ching Sheng, Lim, Meng Hee, Leong, Mohd Salman, Al-Obaidi, Salah Mahdi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738058/
https://www.ncbi.nlm.nih.gov/pubmed/29261689
http://dx.doi.org/10.1371/journal.pone.0189143

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738058/
https://www.ncbi.nlm.nih.gov/pubmed/29261689
http://dx.doi.org/10.1371/journal.pone.0189143

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