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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Bibliographic Details
Published in:Knowledge-based systems Vol. 296; p. 111922
Main Authors: Chen, Zhuohang, Liu, Shen, Li, Chao, Chang, Yuanhong, Chen, Jinglong, Feng, Gaoshan, He, Shuilong
Format: Journal Article
Language:English
Published: Elsevier B.V 19.07.2024
Subjects:
Domain shift
Fault diagnosis
Graph autoencoder
Self-supervised learning
Fault diagnosis
Domain shift
Graph autoencoder
Self-supervised learning
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary: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.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2024.111922