Cargando…
Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis
The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning...
Autores principales: | Bienefeld, Christoph, Becker-Dombrowsky, Florian Michael, Shatri, Etnik, Kirchner, Eckhard |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10528534/ https://www.ncbi.nlm.nih.gov/pubmed/37761577 http://dx.doi.org/10.3390/e25091278 |
Ejemplares similares
-
Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion
por: Zhu, Huibin, et al.
Publicado: (2021) -
Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data
por: Chao, Ko-Chieh, et al.
Publicado: (2022) -
Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
por: Wang, Xiaodong, et al.
Publicado: (2020) -
Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing
por: Kim, Taeyun, et al.
Publicado: (2021) -
Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing
por: Shao, Xiaorui, et al.
Publicado: (2022)