Proportional periodic sampling for cross-load bearing fault diagnosis
Author:
Funder
National Natural Science Foundation of China
National Key R &D Program of China
The Key R &D Program of Changsha
Postgraduate Scientific Research Innovation Project of Hunan Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13042-024-02233-0.pdf
Reference35 articles.
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3. Qiu S, Cui X, Ping Z, Shan N, Li Z, Bao X, Xu X (2023) Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: a review. Sensors 23(3):1305
4. Mikołajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, pp 117–122
5. Zheng J, Yang C, Zheng F, Jiang B (2022) A rolling bearing fault diagnosis method using multi-sensor data and periodic sampling. In: 2022 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1–6
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