A Comparative Study of Rolling Bearing Fault Classification Using CWT-CNN and STFT-CNN Methods
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9264-5_11
Reference12 articles.
1. Wei Y, Li Y, Xu M, Huang W (2019) A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy 21:4–10
2. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning method. Expert Syst Appl 38:1876–1886
3. Ben Ali J, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27
4. Toma RN, Kim CH, Kim JM (2021) Bearing fault classification using ensemble empirical mode decomposition and convolutional neural network. Electronics (Switzerland) 10(11)
5. Saxena M, Bannet O, Gupta M, Rajoria RP (2016) Bearing fault monitoring using CWT based vibration signature. Procedia Eng 144:234–241
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