Impact of Deep Learning Optimizers and Hyperparameter Tuning on the Performance of Bearing Fault Diagnosis
Deep learning has recently resulted in remarkable performance improvements in machine fault diagnosis using only raw input vibration signals without signal preprocessing. However, research on machine fault diagnosis using deep learning has primarily focused on model architectures, even though optimi...
Main Authors: | Seongjae Lee, Taehyoun Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10141610/ |
Similar Items
-
Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning
by: Saeed Iqbal, et al.
Published: (2022-11-01) -
Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs
by: Jacob Hendriks, et al.
Published: (2021-04-01) -
Practical hyperparameters tuning of convolutional neural networks for EEG emotional features classification
by: Samia Mezzah, et al.
Published: (2023-05-01) -
Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization
by: Mikolaj Wojciuk, et al.
Published: (2024-03-01) -
A Deep Learning Based Fault Diagnosis Method With Hyperparameter Optimization by Using Parallel Computing
by: Chaozhong Guo, et al.
Published: (2020-01-01)