Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5129087 | Procedia Manufacturing | 2016 | 12 Pages |
Abstract
Although the orbit analysis (orbit shape and size) is commonly used to diagnose rotating machinery, the diagnosis heavily depends on the expert knowledge or experience due to the difficulties of extracting mathematical features for data-driven approaches. Therefore, in this paper, we propose an autonomous orbit pattern recognition algorithm using the deep learning method on shaft orbit shape images. In details, the convolutional neural network is implemented to construct weights between neurons and to generate the entire structure of the neural network. Then, the created network enables us to classify fault modes of rotating machinery via orbit images. Furthermore, we demonstrate the proposed framework through a rotating testbed.
Related Topics
Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
Haedong Jeong, Seungtae Park, Sunhee Woo, Seungchul Lee,