Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder
Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately,...
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| Veröffentlicht in: | Knowledge-based systems Jg. 296; S. 111922 |
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Elsevier B.V
19.07.2024
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| Abstract | Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately, data collected from equipment often lacks labels, leading to a scarcity of fault data. Furthermore, an additional significant challenge is the feature domain shift resulting from speed variation. To address this, we propose a self-supervised paradigm based on an asymmetric graph autoencoder for fault diagnosis under domain shift, aiming to mine valuable health information from unlabeled data. Unlike Euclidean-based methods, the proposed method transforms time series samples into graphs and extracts domain invariant features through information interaction between nodes. To efficiently mine unlabeled data and enhance generalization, the self-supervised learning paradigm utilizes an asymmetric graph autoencoder architecture. This architecture includes an encoder that learns self-supervised representations from unlabeled samples and a lightweight decoder that predicts the original input. Specifically, we mask a portion of input samples and predict the original input from learned self-supervised representations. In downstream task, the pre-trained encoder is fine-tuned using limited labeled data for specific fault diagnosis task. The proposed method is evaluated on three mechanical fault simulation experiments, and the results demonstrate the its superiority and potential. |
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| AbstractList | Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately, data collected from equipment often lacks labels, leading to a scarcity of fault data. Furthermore, an additional significant challenge is the feature domain shift resulting from speed variation. To address this, we propose a self-supervised paradigm based on an asymmetric graph autoencoder for fault diagnosis under domain shift, aiming to mine valuable health information from unlabeled data. Unlike Euclidean-based methods, the proposed method transforms time series samples into graphs and extracts domain invariant features through information interaction between nodes. To efficiently mine unlabeled data and enhance generalization, the self-supervised learning paradigm utilizes an asymmetric graph autoencoder architecture. This architecture includes an encoder that learns self-supervised representations from unlabeled samples and a lightweight decoder that predicts the original input. Specifically, we mask a portion of input samples and predict the original input from learned self-supervised representations. In downstream task, the pre-trained encoder is fine-tuned using limited labeled data for specific fault diagnosis task. The proposed method is evaluated on three mechanical fault simulation experiments, and the results demonstrate the its superiority and potential. |
| ArticleNumber | 111922 |
| Author | Chen, Jinglong Chen, Zhuohang Li, Chao Chang, Yuanhong Feng, Gaoshan Liu, Shen He, Shuilong |
| Author_xml | – sequence: 1 givenname: Zhuohang orcidid: 0000-0002-6721-8640 surname: Chen fullname: Chen, Zhuohang organization: State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China – sequence: 2 givenname: Shen surname: Liu fullname: Liu, Shen organization: State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China – sequence: 3 givenname: Chao surname: Li fullname: Li, Chao organization: State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China – sequence: 4 givenname: Yuanhong orcidid: 0000-0003-4655-9400 surname: Chang fullname: Chang, Yuanhong organization: State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China – sequence: 5 givenname: Jinglong orcidid: 0000-0002-9805-9849 surname: Chen fullname: Chen, Jinglong email: jlstrive2008@mail.xjtu.edu.cn organization: State Key Laboratory for Manufacturing and Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China – sequence: 6 givenname: Gaoshan surname: Feng fullname: Feng, Gaoshan organization: Dongfeng Liuzhou Motor Co., Ltd., Liuzhou 545005, China – sequence: 7 givenname: Shuilong surname: He fullname: He, Shuilong organization: School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China |
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| Cites_doi | 10.1109/JIOT.2022.3163606 10.1016/j.ymssp.2017.03.034 10.1016/j.ymssp.2023.110462 10.1016/j.knosys.2022.109393 10.1016/j.compind.2021.103557 10.1016/j.jsv.2015.03.018 10.1109/TIM.2021.3127634 10.1016/j.eswa.2023.120696 10.1016/j.aiopen.2021.01.001 10.1016/j.isatra.2021.10.025 10.1016/j.knosys.2021.107978 10.1016/j.knosys.2022.109846 10.1109/JIOT.2022.3181737 10.1016/j.ymssp.2022.109174 10.1016/j.compind.2022.103810 10.1016/j.ymssp.2022.110071 10.1016/j.ress.2021.108126 10.1016/j.isatra.2021.02.042 10.1016/j.jmsy.2022.08.007 10.1016/j.isatra.2022.04.043 10.1109/TPAMI.2020.2992393 10.1109/TNNLS.2020.2978386 10.1016/j.knosys.2022.110008 10.1109/TPAMI.2022.3152247 |
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| Keywords | Fault diagnosis Domain shift Graph autoencoder Self-supervised learning |
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| Title | Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder |
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