Cargando…
Deep Learning Approach for Vibration Signals Applications
This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression...
Autores principales: | Chen, Han-Yun, Lee, Ching-Hung |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201341/ https://www.ncbi.nlm.nih.gov/pubmed/34200400 http://dx.doi.org/10.3390/s21113929 |
Ejemplares similares
-
Speech extraction from vibration signals based on deep learning
por: Wang, Li, et al.
Publicado: (2023) -
A Waveform Mapping-Based Approach for Enhancement of Trunk Borers’ Vibration Signals Using Deep Learning Model
por: Shi, Haopeng, et al.
Publicado: (2022) -
Reduced Order Modeling of Nonlinear Vibrating Multiphysics Microstructures with Deep Learning-Based Approaches
por: Gobat, Giorgio, et al.
Publicado: (2023) -
Application of Deep Reinforcement Learning to NS-SHAFT Game Signal Control
por: Chang, Ching-Lung, et al.
Publicado: (2022) -
A Deep Learning Model with Signal Decomposition and Informer Network for Equipment Vibration Trend Prediction
por: Wang, Huiyun, et al.
Publicado: (2023)