Cargando…
Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults
Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method can increase the fault s...
Autores principales: | Zhou, Funa, Park, Ju H., Wen, Chenglin, Hu, Po |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6021969/ https://www.ncbi.nlm.nih.gov/pubmed/29865291 http://dx.doi.org/10.3390/s18061804 |
Ejemplares similares
-
A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery
por: Zhou, Funa, et al.
Publicado: (2018) -
A Multiscale Recursive Attention Gate Federation Method for Multiple Working Conditions Fault Diagnosis
por: Zhang, Zhiqiang, et al.
Publicado: (2023) -
Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate
por: Hasanzadeh, Navid, et al.
Publicado: (2020) -
On the stability of a class of slowly varying systems
por: Naser, M. F. M., et al.
Publicado: (2018) -
Investigation of the beam impedance of a slowly varying waveguide
por: Jones, R M, et al.
Publicado: (1996)