Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion†
AbstractAiming 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Zhang, L.; Tao, J. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion. Algorithms 2018, 11, 21.
Zhang L, Tao J. Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion. Algorithms. 2018; 11(2):21.Chicago/Turabian Style
Zhang, Lijun; Tao, Junyu. 2018. "Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion." Algorithms 11, no. 2: 21.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.