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ORIGINAL RESEARCH article

Front. Neurorobot.
Volume 18 - 2024 | doi: 10.3389/fnbot.2024.1499703
This article is part of the Research Topic Multi-source and Multi-domain Data Fusion and Enhancement: Methods, Evaluation, and Applications View all 3 articles

Real-time Fault Detection for IIoT Facilities using GA-Att-LSTM based on Edge-cloud Collaboration

Provisionally accepted
Jiuling Dong Jiuling Dong 1Zehui Li Zehui Li 1*Yuanshuo Zheng Yuanshuo Zheng 2*Jingtang Luo Jingtang Luo 3*Min Zhang Min Zhang 1*Xiaolong Yang Xiaolong Yang 1*
  • 1 University of Science and Technology Beijing, Beijing, China
  • 2 Hainan Normal University, Haikou, China
  • 3 State Grid Sichuan Economic Research Institute, Chengdu, Sichuan Province, China

The final, formatted version of the article will be published soon.

    With the rapid development of Industrial Internet of Things (IIoT) technology, various IIoT devices are generating large amounts of industrial sensor data that are spatiotemporally correlated and heterogeneous from multi-source and multi-domain. This poses a challenge to current detection algorithms. Therefore, this paper proposes an improved long short-term memory (LSTM) neural network model based on the genetic algorithm, attention mechanism and edge-cloud collaboration (GA-Att-LSTM) framework is proposed to detect anomalies of IIoT facilities. Firstly, an edge-cloud collaboration framework is established to real-time process a large amount of sensor data at the edge node in real time, which reduces the time of uploading sensor data to the cloud platform. Secondly, to overcome the problem of insufficient attention to important features in the input sequence in traditional LSTM algorithms, we introduce an attention mechanism to adaptively adjust the weights of important features in the model. Meanwhile, a genetic algorithm optimized hyperparameters of the LSTM neural network is proposed to transform anomaly detection into a classification problem and effectively extract the correlation of time-series data, which improves the recognition rate of fault detection. Finally, the proposed method has been evaluated on a publicly available fault database. The results indicate an accuracy of 99.6%, an F1-score of 84.2%, a precision of 89.8%, and a recall of 77.6%, all of which exceed the performance of five traditional machine learning methods.

    Keywords: Internet of Things, fault detection, Edge-cloud collaboration, attention mechanism, LSTM

    Received: 21 Sep 2024; Accepted: 28 Oct 2024.

    Copyright: © 2024 Dong, Li, Zheng, Luo, Zhang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Zehui Li, University of Science and Technology Beijing, Beijing, China
    Yuanshuo Zheng, Hainan Normal University, Haikou, China
    Jingtang Luo, State Grid Sichuan Economic Research Institute, Chengdu, Sichuan Province, China
    Min Zhang, University of Science and Technology Beijing, Beijing, China
    Xiaolong Yang, University of Science and Technology Beijing, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.