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- W4221136108 abstract "Since the manual extraction of features is not sufficient to accurately characterize the health status of rolling bearings, machine learning algorithms are gradually being used for fault diagnosis of bearings, which can adaptively learn the required features from the input data. In this paper, <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>k</mi> </math> -nearest neighbor, support vector machines, and convolutional neural networks are successfully applied to the fault diagnosis of bearings, for the benefit of achieving the detection and early warning of bearing fatigue damage. The original samples are segmented into semioverlapping samples. When using <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <mi>k</mi> </math> -nearest neighbor and support vector machines as early warning models, we searched their hyperparameters with random search and grid search, and the results showed that support vector machines could achieve 87.1% of bearing detection accuracy and <math xmlns=http://www.w3.org/1998/Math/MathML id=M3> <mi>k</mi> </math> -nearest neighbor could achieve 100% of detection accuracy. When convolutional neural networks are used as the early warning model, the accuracy can reach 99.75%." @default.
- W4221136108 created "2022-04-03" @default.
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- W4221136108 date "2022-03-24" @default.
- W4221136108 modified "2023-10-14" @default.
- W4221136108 title "Data-Driven Fatigue Damage Monitoring and Early Warning Model for Bearings" @default.
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- W4221136108 doi "https://doi.org/10.1155/2022/7611670" @default.
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