Aalto Gear Fault datasets for deep-learning based diagnosisMendeley DataMendeley DataMendeley Data

Aalto Gear Fault datasets for deep-learning based diagnosisMendeley DataMendeley DataMendeley Data

Accurate system health state prediction through deep learning requires extensive and varied data. However, real-world data scarcity poses a challenge for developing robust fault diagnosis models. This study introduces two extensive datasets, Aalto Shim Dataset and Aalto Gear Fault Dataset, collected...

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Bibliographic Details
Main Authors: Zacharias Dahl, Aleksanteri Hämäläinen, Aku Karhinen, Jesse Miettinen, Andre Böhme, Samuel Lillqvist, Sampo Haikonen, Raine Viitala
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Data in Brief
Subjects:
Torsional vibration
Lateral vibration
Vibration dataset
Condition monitoring
Intelligent fault diagnosis
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340924011338
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