Articles | Volume 15, issue 1
https://doi.org/10.5194/ms-15-87-2024
https://doi.org/10.5194/ms-15-87-2024
Research article
 | 
19 Feb 2024
Research article |  | 19 Feb 2024

A convolutional neural-network-based diagnostic framework for industrial bearing

Bowen Yu and Chunli Xie

Related subject area

Subject: Mechanisms and Robotics | Techniques and Approaches: Mathematical Modeling and Analysis
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Cited articles

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Chen, X., Zhang, B., and Gao, D.: Bearing fault diagnosis base on multi-scale CNN and LSTM model, J. Intell. Manuf., 32, 971–987, https://doi.org/10.1007/s10845-020-01600-2, 2021. 
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Short summary
In this study, we introduce an innovative diagnostic framework tailored to industrial bearings that facilitates automated feature extraction and enables end-to-end fault detection. Our validation across multiple bearing vibration datasets confirms the framework's superiority in handling complex and non-stationary signals in industrial environments.