Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
AbstractIn order to improve the accuracy of the fault diagnosis of roller bearings, this paper proposes a kind of fault diagnosis algorithm based on manifold learning combined with a wavelet neural network. First, a high-dimensional feature signal set is obtained using a conventional feature extraction algorithm; second, an improved Laplacian characteristic mapping algorithm is proposed to reduce the dimensions of the characteristics and obtain an effective characteristic signal. Finally, the processed characteristic signal is inputted into the constructed wavelet neural network whose output is the types of fault. In the actual experiment of recognizing data sets on roller bearing failures, the validity and accuracy of the method for diagnosing faults was verified. 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
Wu, L.; Yao, B.; Peng, Z.; Guan, Y. Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning. Appl. Sci. 2017, 7, 158.
Wu L, Yao B, Peng Z, Guan Y. Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning. Applied Sciences. 2017; 7(2):158.Chicago/Turabian Style
Wu, Lifeng; Yao, Beibei; Peng, Zhen; Guan, Yong. 2017. "Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning." Appl. Sci. 7, no. 2: 158.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.