cyber-physical system

KrakenBox

KrakenBox

Deep learning-based error detector for industrial cyber-physical systems
Sheng Ding, Tagir Fabarisov, Philipp Grimmeisen, Andrey Morozov
Deep learning-based error detection methods outperform traditional methods because of the continuously increasing complexity of technical systems and inherent flexibility and scalability of Deep Learning techniques. This article introduces the KrakenBox – an autonomous Deep Learning-based error detector for industrial Cyber-Physical Systems (CPS). It exploits a lightweight, Long Short-Term Memory (LSTM) network capable of online error detection that can be deployed on an embedded platform such as NVIDIA Jetson AGX Xavier or even Google Coral Edge TPU. This article describes the architecture of the KrakenBox and demonstrates its application with two case studies.
Industrie 4.0 Management | Volume 37 | 2021 | Edition 6 | Pages 27-31
The Appropriate Degree of Autonomy in Cyber-Physical Production Systems

The Appropriate Degree of Autonomy in Cyber-Physical Production Systems

Norbert Gronau ORCID Icon
Existing factories face multiple problems due to their hierarchical structure of decision making and control. Cyber-physical systems principally allow to increase the degree of autonomy to new heights. But which degree of autonomy is really useful and beneficiary? This paper differentiates diverse definitions of autonomy and approaches to determine them. Some experimental findings in a lab environment help to answer the question raised in this paper.
Industrie 4.0 Management | Volume 34 | 2018 | Edition 6 | Pages 7-12 | DOI 10.30844/I40M_18-6_7-12
Artificial Intelligence gives wings Cyber-Physical Systems

Artificial Intelligence gives wings Cyber-Physical Systems

Volker Gruhn
Cyber-Physical Systems (CPS) are an example of the close connection between the digital and the real world. This connection makes the development of the systems more complex. Methods of Artificial Intelligence (AI) such as machine learning help companies to use these systems for new application scenarios. Image and speech recognition capabilities enable new, closer forms of cooperation between humans and CPS that previously did not work for occupational safety reasons. At the same time, machine learning enhances the cognitive abilities of CPS. They can work independently in situations which are difficult to plan.
Industrie 4.0 Management | Volume 34 | 2018 | Edition 6 | Pages 45-48 | DOI 10.30844/I40M_18-6_45-48
The Internet of Load Carriers

The Internet of Load Carriers

Smart load carriers and its cloud-based service system
Martina Romer, Sebastian Meißner
The transformation of business models, based on the digitalisation of products, is becoming more and more in the focus of the companies and is also affecting the Load Carrier branch. Intelligent components are integrated into the products, to collect and analyse data in order to create new services. The digital transformation of intelligent products into cyber-physical-systems, which includes a setup of a cloud-based Service System, enables traditional Load Carrier manufacturer to develop new business areas to optimize the supply chain of their customers by offering new services.
Industrie 4.0 Management | Volume 34 | 2018 | Edition 3 | Pages 13-16
Migration Towards an Intelligent Production System

Migration Towards an Intelligent Production System

Representation of a Decision Model for Value-Based Integration of Cyber-Physical Systems into Production
Jonas Gutjahr, Eva Bogner, Markus Bäumler
The Smart Factory describes the future way of production where intelligent cyberphysical systems (CPS) form the technical foundation. Regarding this vision, decision-makers out of the industry are purchasing for help in order to implement CPS successfully. A method based on the strategical orientation of companies is presented, with which potential CPS applications can be identified and an associated investment decision can be supported.
Industrie 4.0 Management | Volume 33 | 2017 | Edition 3 | Pages 31-34