Cargando…
A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven...
Autores principales: | Toma, Rafia Nishat, Piltan, Farzin, Kim, Jong-Myon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8706012/ https://www.ncbi.nlm.nih.gov/pubmed/34960552 http://dx.doi.org/10.3390/s21248453 |
Ejemplares similares
-
Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
por: Toma, Rafia Nishat, et al.
Publicado: (2022) -
A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions
por: Toma, Rafia Nishat, et al.
Publicado: (2022) -
Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers
por: Toma, Rafia Nishat, et al.
Publicado: (2020) -
Blade Rub-Impact Fault Identification Using Autoencoder-Based Nonlinear Function Approximation and a Deep Neural Network
por: Prosvirin, Alexander E., et al.
Publicado: (2020) -
Bearing Fault Diagnosis Using a Hybrid Fuzzy V-Structure Fault Estimator Scheme
por: Piltan, Farzin, et al.
Publicado: (2023)