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
Abstract
Data-driven fault detection techniques have attracted extensive attention in engineering, industry and many other areas in recent years. In many real applications, the following situation often occurs: data for certain types of faults (unseen faults) are not available to train models that are used for fault detection. Such a scenario can occur when data collection becomes highly time-consuming or destructive. To address this challenging problem, a novel fault detection method using zero-shot learning (ZSL) is proposed in this paper, which contains three phases: feature extraction, label embedding, and feature embedding. The method first extracts features from raw signals by applying a one-dimensional convolutional neural network (1D CNN), then builds semantic descriptions (human-defined) as fault attributes shared between seen faults and unseen faults, and finally uses a bi-linear compatibility function to find the highest-ranking fault type. The proposed semantic space based zero-shot learning with 1D CNN is called SSB-ZSL-1DCNN. The cosine distance is used to measure the similarity between feature embeddings and fault attributes. An important characteristic of SSB-ZSL-1DCNN is that the model, trained using only samples of seen faults, can be used to detect unseen defects. To evaluate the proposed method, two case studies are designed based on two well-known benchmarks (the Tennessee-Eastman chemical control process and the rolling bearing experiments at the Case Western Reserve University, respectively). The results demonstrate that the proposed method shows remarkable performance in detecting unseen faults.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:193658 |