File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Full metadata record

DC Field Value Language
dc.citation.conferencePlace US -
dc.citation.title 44th SME North American Manufacturing Research Conference (NAMRC) -
dc.contributor.author Jeong, Haedong -
dc.contributor.author Park, Seungtae -
dc.contributor.author Woo, Sunhee -
dc.contributor.author Lee, Seungchul -
dc.date.accessioned 2023-12-19T20:36:51Z -
dc.date.available 2023-12-19T20:36:51Z -
dc.date.created 2017-01-04 -
dc.date.issued 2016-06-29 -
dc.description.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. -
dc.identifier.bibliographicCitation 44th SME North American Manufacturing Research Conference (NAMRC) -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/36844 -
dc.language 영어 -
dc.publisher SME -
dc.title Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images -
dc.type Conference Paper -
dc.date.conferenceDate 2016-06-27 -

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.