Industrial cyber physical systems: A survey for control-engineering tools

Jbair, Mohammad; Ahmad, Bilal; Ahmad, Mus'ab H.; Harrison, Robert

doi:10.1109/icphys.2018.8387671

Cited by 34 publications

(20 citation statements)

References 7 publications

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“…Cyber-physical systems (CPS) are distributed, heterogeneous systems connected via networks and are usually associated with the concept of the IoT [9], whereas CPPS mechatronic components are coupled via networks to computational entities that enable production systems to adapt when changes occur throughout their lifecycles [10]. CPPS is formed by the integration of physical and digital systems across all levels of manufacturing enterprises that collaborate to form intelligent and responsive production systems [11].…”

Section: B Cpps and Emerging Digital Twinsmentioning

confidence: 99%

A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems

2021

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The potential benefits of the adoption of cyber-physical production systems (CPPSs) and their significant role in enabling smart manufacturing is well recognized today. However, it is less clear how such CPPS can be most effectively and consistently engineered and maintained throughout their lifecycle due to the existing divide in the information technology (IT) and operational technology (OT) landscape and ad hoc integration practices that result in inconsistent data and data models at various levels of manufacturing processes. The work presented in this article addresses this problem by envisioning a connective framework to support the engineering of CPPS through the use of a set of digital twins consistent with the real system throughout its lifecycle, not just used in the design and deployment phases. A review of the latest perspectives on using digital integration frameworks, methods, and solutions for lifecycle engineering of CPPS is provided in this article. This article demonstrates how a suitable framework, named SIMPLE, can be realized to effectively address the lack of consistent data models throughout the engineering lifecycle, including implementation details and example cases developed by the authors at the Warwick Manufacturing Group (WMG) in selected industrial Manuscript

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Section: B Cpps and Emerging Digital Twinsmentioning

confidence: 99%

A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems

2021

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Section: Motivationmentioning

confidence: 99%

“…Current computing platform architectures in industry consist of different types of processing elements, e.g., programmable logic controllers or industrial personal computers, which run tasks that are typically high-critical [17]. Moreover, the processing elements currently used in industry have major limitations, such as lack of network capabilities, no support for web services, limited resource capacities, or plug-andplay features [17]. The paradigm of fog computing not only eliminates these limitations but also brings increased productivity and flexibility, mass customization, reduced timeto-market, improved product quality, innovations, and new business models [18], being an architectural means to realize the Operational and Information Technologies (OT/IT) convergence [19].…”

Section: Motivationmentioning

confidence: 99%

Towards Extensibility-Aware Scheduling of Industrial Applications on Fog Nodes

Barzegaran

Karagiannis

Avasalcai

et al. 2020

2020 IEEE International Conference on Edge Computing (EDGE)

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Fog computing has been identified as an enabler for many modern technologies like connected vehicles and the Industrial Internet of Things (IIoT). Such technologies are characterized by the integration of applications with different levels of criticality on shared platforms, which are referred to as mixed-criticality systems. Mixed-criticality systems typically use static scheduling for critical tasks; however, static scheduling is not suitable for scenarios where fog nodes run dynamic noncritical applications that implement, e.g., maintenance checks and data analytics.To address this challenge, in this paper, we differentiate between critical tasks that are statically allocated (called "native") and dynamic non-critical tasks that may migrate across fog nodes (called "temporary"). We propose a static scheduling approach that maximizes the number of temporary tasks that can be added at runtime, without negatively impacting the already scheduled native tasks. This approach enables fog nodes to become more suitable for IIoT environments by configuring them with extensible schedules for the native tasks. To evaluate our approach, we perform experiments considering several test cases, which show that given a number of native tasks, the generated extensible schedules enable the fog nodes to run a larger number of temporary tasks at the same time. Furthermore, the extensible schedules exhibit 7.8% less missed deadlines (on average), compared to the non-extensible schedules.

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“…Automatic code generation is a transformation of machine control behaviour, defined in a higher level of abstract during design phase, into a code that can be implemented directly into the automation system [7]. Typically, machine behaviour data are available and can be extracted from design tools that are used to define control behavior of a machine [8].…”

Section: Automatic Plc Programming Generation Practicesmentioning

confidence: 99%

“…In addition to the academic research, a number of control vendors also developed automatic code generation tools, such as Mitsubishi Adroit Process Suite (MAPS), Schneider Unity Application Generator (UAG), Beckhoff TwinCAT automation interface and other vendors [8]. These tools aim to automatically generate the PLC code based on a predefined workflow that needs to be configured by the PLC programmer, this workflow is usually integrated within the tool.…”

Section: Automatic Plc Programming Generation Practicesmentioning

confidence: 99%