Extreme learning machine and ensemble techniques for classification of rolling element bearing defects
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Engineering
Link
https://link.springer.com/content/pdf/10.1007/s41872-022-00196-1.pdf
Reference41 articles.
1. Akhenia P, Bhavsar K, Panchal J, Vakharia V (2021) Fault severity classification of ball bearing using SinGAN and deep convolutional neural network. Proc Inst Mech Engineers Part C J Mech Eng Sci. https://doi.org/10.1177/09544062211043132
2. Breiman L (1999) Random forests. Mach Learn 45:1–35
3. Chen C, Mo C (2004) A method for intelligent fault diagnosis of rotating machinery. Digit Signal Process A Rev J 14:203–217
4. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844
5. Huang G, Zhu Q, Siew C (2004) Extreme learning machine : a new learning scheme of feed forward neural networks. IEEE Int Jt Conf Neural Netw 2:985–990
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