基于边缘-云协作,使用 GA-Att-LSTM 对 IIoT 设施进行实时故障检测。

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Neurorobotics Pub Date : 2024-11-11 eCollection Date: 2024-01-01 DOI:10.3389/fnbot.2024.1499703
Jiuling Dong, Zehui Li, Yuanshuo Zheng, Jingtang Luo, Min Zhang, Xiaolong Yang
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引用次数: 0

摘要

随着工业物联网(IIoT)技术的快速发展,各种 IIoT 设备正在产生大量的工业传感器数据,这些数据具有时空相关性,并且来自多源和多域的异构数据。这给当前的检测算法带来了挑战。因此,本文提出了一种基于遗传算法、注意力机制和边缘云协作(GA-Att-LSTM)框架的改进型长短期记忆(LSTM)神经网络模型,用于检测 IIoT 设备的异常。首先,建立边缘-云协作框架,实时处理边缘节点的大量传感器数据,减少传感器数据上传到云平台的时间。其次,为了克服传统 LSTM 算法中对输入序列中重要特征关注不够的问题,我们引入了关注机制,自适应地调整模型中重要特征的权重。同时,提出了一种遗传算法优化的 LSTM 神经网络超参数,将异常检测转化为分类问题,有效提取了时间序列数据的相关性,提高了故障检测的识别率。最后,在一个公开的故障数据库中对所提出的方法进行了评估。结果表明,该方法的准确率为 99.6%,F1 分数为 84.2%,精确度为 89.8%,召回率为 77.6%,均超过了五种传统机器学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration.

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.

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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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