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- Title
Research on fault diagnosis of rolling bearings in roller-to-roller printing units based on siamese network.
- Authors
Xu, Zhuofei; Zhang, Chanchan; Liu, Shanhui; Zhang, Wu; Zhang, Yafeng
- Abstract
To realize a fault diagnosis of rolling bearings in a real R2R printing unit, a method based on Siamese Network is proposed in this work. First, vibration signals in rolling bearing were changed into a series of time–frequency spectra with Continuous Wavelet Transform, and thus the frequency components with time in various scales can be reflected as images. Siamese Networks with sub-nets composed of both Convolutional Neural Network (CNN) and Depth-wise Separable Convolution Network (DSCN) were proposed and established for fault diagnosis; meanwhile, fault samples were divided into sample twins to solve the problem of small samples. As to a database of rolling bearings, different kinds of faults with various degree, rotary speed and added noise were distinguished with both SN-CNN and SN-DSCN models successfully. Then an experiment for a R2R unit in printing press is also taken, there are all 7 classes of samples to be identified, and each group contains few numbers of samples. From this work, it can be seen that SN-CNN and SN-DSCN both can realize a fault diagnosis of rolling bearings in printing units based on 20 samples, which can be seen as a limited sample learning mission. Besides, SN-DSCN is proved to have a less time in training process compare to SN-CNN.
- Subjects
ARTIFICIAL neural networks; ROLLER bearings; FAULT diagnosis; CONVOLUTIONAL neural networks; WAVELET transforms; PRINTING presses
- Publication
Journal of Low Frequency Noise, Vibration & Active Control, 2023, Vol 42, Issue 1, p403
- ISSN
1461-3484
- Publication type
Article
- DOI
10.1177/14613484221119897