Cyber Physical Systems for Industry 4.0: Towards Real Time Virtual Reality in Smart Manufacturing

AbstractThis work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.

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Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s089006042100010x ◽

2021 ◽

Vol 35 (2) ◽

pp. 251-264

Author(s):

Dimitrios Boursinos ◽

Xenofon Koutsoukos

Keyword(s):

Neural Networks ◽

Distance Learning ◽

Real Time ◽

Speaker Recognition ◽

Deep Neural Networks ◽

Cyber Physical Systems ◽

Error Rates ◽

Traffic Sign ◽

Conformal Prediction ◽

Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

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Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations

2020 IEEE Real-Time Systems Symposium (RTSS) ◽

10.1109/rtss49844.2020.00028 ◽

2020 ◽

Author(s):

Lin Zhang ◽

Xin Chen ◽

Fanxin Kong ◽

Alvaro A. Cardenas

Keyword(s):

Real Time ◽

Cyber Physical Systems ◽

Physical Systems ◽

Linear Approximations

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The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽

10.3390/j4020012 ◽

2021 ◽

Vol 4 (2) ◽

pp. 147-153

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Antonio Royo ◽

Juan Carlos Sánchez

Keyword(s):

Decision Making ◽

Data Acquisition ◽

Real Time ◽

Failure Detection ◽

Cyber Physical Systems ◽

Future Research ◽

Time Data ◽

Physical Systems ◽

The Real ◽

Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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Artificial Intelligence and the Internet of Things in Industry 4.0

CCF Transactions on Pervasive Computing and Interaction ◽

10.1007/s42486-021-00057-3 ◽

2021 ◽

Author(s):

Petar Radanliev ◽

David De Roure ◽

Razvan Nicolescu ◽

Michael Huth ◽

Omar Santos

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Physical Systems ◽

Leading Industry ◽

Key Technologies ◽

The Internet Of Things ◽

State Of Art ◽

Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

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Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Sensors ◽

10.3390/s21020487 ◽

2021 ◽

Vol 21 (2) ◽

pp. 487 ◽

Cited By ~ 2

Author(s):

Mahmoud Elsisi ◽

Karar Mahmoud ◽

Matti Lehtonen ◽

Mohamed M. F. Darwish

Keyword(s):

Machine Learning ◽

Smart Grids ◽

Industry 4.0 ◽

Learning Algorithm ◽

Cyber Physical Systems ◽

The Internet ◽

Smart Meters ◽

Industrial Environment ◽

Physical Systems ◽

Smart Machines

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

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