Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Huang, Huiyue; Lei, Yang; Wang, Yuanbin; Xu, Xun

doi:10.1016/j.jmsy.2021.02.010

Cited by 97 publications

(47 citation statements)

References 42 publications

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“…Khandewale et al [ 20 ] applied edge AI and neural compute to develop an object detection system for visually impaired people, and the system will support them for person recognition in future. Huang et al [ 18 ] adopted the edge AI philosophy distributing supervised machine learning, data processing and decision-making tasks among the physical layer, edge layer and cloud layer. They proposed a Digital Twin-driven anomaly detection framework to enable industrial systems to perform anomaly detection in real-time.…”

Section: Related Workmentioning

confidence: 99%

A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence

Zhu

Wang

Zhou³

et al. 2021

World Wide Web

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With the development of telemedicine and edge computing, edge artificial intelligence (AI) will become a new development trend for smart medicine. On the other hand, nearly one-third of children suffer from sleep disorders. However, all existing sleep staging methods are for adults. Therefore, we adapted edge AI to develop a lightweight automatic sleep staging method for children using single-channel EEG. The trained sleep staging model will be deployed to edge smart devices so that the sleep staging can be implemented on edge devices which will greatly save network resources and improving the performance and privacy of sleep staging application. Then the results and hypnogram will be uploaded to the cloud server for further analysis by the physicians to get sleep disease diagnosis reports and treatment opinions. We utilized 1D convolutional neural networks (1D-CNN) and long short term memory (LSTM) to build our sleep staging model, named CSleepNet. We tested the model on our childrens sleep (CS) dataset and sleep-EDFX dataset. For the CS dataset, we experimented with F4-M1 channel EEG using four different loss functions, and the logcosh performed best with overall accuracy of 83.06% and F1-score of 76.50%. We used Fpz-Cz and Pz-Oz channel EEG to train our model in Sleep-EDFX dataset, and achieved an accuracy of 86.41% without manual feature extraction. The experimental results show that our method has great potential. It not only plays an important role in sleep-related research, but also can be widely used in the classification of other time sequences physiological signals.

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Section: Related Workmentioning

confidence: 99%

A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence

Zhu

Wang

Zhou³

et al. 2021

World Wide Web

View full text Add to dashboard Cite

show abstract

“…Currently, Cloud-based Digital Twin frameworks for industrial robotics [35], manufacturing execution systems [36], [37] and smart product-service systems [38] have been developed to provide an insight into smart manufacturing. Edge-based Digital Twins have also been studied in the literature with their benefits for manufacturing such as anomaly detection for automation systems [39] and improving production quality, efficiency and costs for metal additive manufacturing system [40].…”

Section: B Applications In Real Industrial Environmentsmentioning

confidence: 99%

Dissecting the Impact of Information and Communication Technologies on Digital Twins as a Service

et al. 2021

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Recent advances on Edge computing, Network Function Virtualization (NFV) and 5G are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Digital Twin is a key piece in the industrial digital transformation and its benefits are very well studied in the literature. However, designing and implementing a Digital Twin system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. Therefore, prototyping the system is required to gradually validate and optimize Digital Twin solutions. In this work, an Edge Robotics Digital Twin system is implemented as a prototype that embodies the concept of Digital Twin as a Service (DTaaS). Such system enables real-time applications such as visualization and remote control, requiring low-latency and high reliability. The capability of the system to offer potential savings by means of computation offloading are analyzed in different deployment configurations. Moreover, the impact of different wireless channels (e.g., 5G, 4G and WiFi) to support the data exchange between a physical device and its virtual components are assessed within operational Digital Twins. Results show that potentially 16% of CPU and 34% of MEM savings can be achieved by virtualizing and offloading software components in the Edge. In addition, they show that 5G connectivity enables remote control of 20 ms, appearing as the most promising radio access technology to support the main requirements of Digital Twin systems.

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“…For instance, if the digital twin is used to optimize the control parameters, their values can be sent to the physical system as a parameter update. If the digital twin is used for condition monitoring, a shut down signal can be sent to the physical system upon the detection of an anomaly that causes a safety concern [28], or a warning can be sent to the turbine operator to signal an upcoming component failure as a predictive maintenance measure.…”

Section: Definition Of a Digital Twinmentioning

confidence: 99%

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

et al. 2021

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The Industry 4.0 concept of a Digital Twin will bring many advantages for wind energy conversion systems, e.g., in condition monitoring, predictive maintenance and the optimisation of control or design parameters. A virtual replica is at the heart of a digital twin. To construct a virtual replica, appropriate modelling techniques must be selected for the turbine components. These models must be chosen with the intended use case of the digital twin in mind, finding a proper balance between the model fidelity and computational load. This review article presents an overview of the recent literature on modelling techniques for turbine aerodynamics, structure and drivetrain mechanics, the permanent magnet synchronous generator, the power electronic converter and the pitch and yaw systems. For each component, a balanced overview is given of models with varying model fidelity and computational load, ranging from simplified lumped parameter models to advanced numerical Finite Element Method (FEM)-based models. The results of the literature review are presented graphically to aid the reader in the model selection process. Based on this review, a high-level structure of a digital twin is proposed together with a virtual replica with a minimum computational load. The concept of a multi-level hierarchical virtual replica is presented.

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Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Cited by 97 publications

References 42 publications

A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence

A lightweight automatic sleep staging method for children using single-channel EEG based on edge artificial intelligence

Dissecting the Impact of Information and Communication Technologies on Digital Twins as a Service

Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

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