SIMULATION-DRIVEN DEEP CLASSIFICATION OF BEARING FAULTS FROM RAW VIBRATION DATA

SIMULATION-DRIVEN DEEP CLASSIFICATION OF BEARING FAULTS FROM RAW VIBRATION DATA

The industry is moving towards maintenance strategies that consider component health, which require extensive collection and analysis of data. Condition monitoring methods that require manual feature extraction and analysis, become infeasible on an industrial scale. Machine learning algorithms can b...

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Bibliographic Details
Main Authors:	Martin Hemmer, Andreas Klausen, Huynh van Khang, Kjell G. Robbersmyr, Tor I. Waag
Format:	Article
Language:	English
Published:	The Prognostics and Health Management Society 2019-12-01
Series:	International Journal of Prognostics and Health Management
Subjects:	convolutional neural network bearing fault diagnosis deep learning for time-series

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