Cyber-physical system

About: Cyber-physical system is a research topic. Over the lifetime, 11096 publications have been published within this topic receiving 162489 citations. The topic is also known as: CPS.

A review of industry 4.0 manufacturing process security risks

Abstract: The advent of three-dimensional (3D) printing has found a unique and prominent role in Industry 4.0 and is rapidly gaining popularity in the manufacturing industry. 3D printing offers many advantages over conventional manufacturing methods, making it an attractive alternative that is more cost-effective and efficient than conventional manufacturing methods. With the Internet of Things (IoT) at the heart of this new movement, control over manufacturing methods now enters the cyber domain, offering endless possibilities in manufacturing automation and optimization. However, as disruptive and innovative as this may seem, there is grave concern about the cyber-security risks involved. These security aspects are often overlooked, particularly by promising new start-ups and parties that are not too familiar with the risks involved in not having proper cyber-security measures in place. This paper explores some of the cyber-security risks involved in the bridge between industrial manufacturing and Industry 4.0, as well as the associated countermeasures already deployed or currently under development. These aspects are then contextualized in terms of Industry 4.0 in order to serve as a basis for and assist with future development in this field.

A survey on Cyber Physical Energy Systems and their applications on smart grids

Abstract: An important challenge for future energy systems is a new modeling methodology that integrates the cyber and physical components. This model must include the impact of communication networks and further cyber components, besides the relevant information of the physical system, in terms of efficiency, sustainability, reliability, security, and stability. The Cyber Physical Energy Systems (CPES) concept is presented as an interesting alternative to address this issue and its main features are identified. The main CPES research areas are identified as: modelling energy systems, energy efficiency, energy resource management, and energy control. In this work, the advantages of the CPES approach are shown in order to address current challenges in future energy systems. Smart grids, based on microgrids and distributed generation concepts, are identified as an interesting application of the CPES. A new proposal for modeling smart grids based on the CPES approach is introduced.

Intelligent Small Object Detection for Digital Twin in Smart Manufacturing With Industrial Cyber-Physical Systems

Abstract: Recently, along with several technological advancements in cyber-physical systems, the revolution of Industry 4.0 has brought in an emerging concept named digital twin (DT), which shows its potential to break the barrier between the physical and cyber space in smart manufacturing. However, it is still difficult to analyze and estimate the real-time structural and environmental parameters in terms of their dynamic changes in digital twinning, especially when facing detection tasks of multiple small objects from a large-scale scene with complex contexts in modern manufacturing environments. In this article, we focus on a small object detection model for DT, aiming to realize the dynamic synchronization between a physical manufacturing system and its virtual representation. Three significant elements, including equipment, product, and operator, are considered as the basic environmental parameters to represent and estimate the dynamic characteristics and real-time changes in building a generic DT system of smart manufacturing workshop. A hybrid deep neural network model, based on the integration of MobileNetv2, YOLOv4, and Openpose, is constructed to identify the real-time status from physical manufacturing environment to virtual space. A learning algorithm is then developed to realize the efficient multitype small object detection based on the feature integration and fusion from both shallow and deep layers, in order to facilitate the modeling, monitoring, and optimizing of the whole manufacturing process in the DT system. Experiments and evaluations conducted in three different use cases demonstrate the effectiveness and usefulness of our proposed method, which can achieve a higher detection accuracy for DT in smart manufacturing.

Big Data Analysis in Smart Manufacturing: A Review

Abstract: The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.