An effective zero-shot learning approach for intelligent fault detection using 1D CNN

Zhang, S., Wei, H. orcid.org/0000-0002-4704-7346 and Ding, J. (2023) An effective zero-shot learning approach for intelligent fault detection using 1D CNN. Applied Intelligence, 53 (12). pp. 16041-16058. ISSN 0924-669X

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Item Type: Article
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© The Author(s) 2022. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Keywords: fault detection; zero-shot learning; deep learning; semantic description; label embedding; convolutional neural network
Dates:
  • Published: June 2023
  • Published (online): 1 December 2022
  • Accepted: 11 November 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/H00453X/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/I011056/1
NATURAL ENVIRONMENT RESEARCH COUNCIL
NE/V002511/1
Depositing User: Symplectic Sheffield
Date Deposited: 23 Nov 2022 11:40
Last Modified: 05 Jun 2023 15:56
Status: Published
Publisher: Springer
Refereed: Yes
Identification Number: 10.1007/s10489-022-04342-1
Open Archives Initiative ID (OAI ID):

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