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Algorithms 2018, 11(2), 21; doi:10.3390/a11020021

Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion

National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
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Received: 31 December 2017 / Revised: 3 February 2018 / Accepted: 9 February 2018 / Published: 11 February 2018
(This article belongs to the Special Issue Advanced Artificial Neural Networks)
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Abstract

Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM) model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets. View Full-Text
Keywords: deep groove ball bearings; degeneration model; neural network; feature fusion deep groove ball bearings; degeneration model; neural network; feature fusion
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, L.; Tao, J. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion. Algorithms 2018, 11, 21.

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