SAMBA: A self-aware health monitoring architecture for distributed industrial systems

Siafara, Lydia C.; Kholerdi, Hedyeh A.; Братухин, А. Г.; TaheriNejad, Nima; Wendt, Alexander; Jantsch, Axel; Treytl, Albert; Sauter, Thilo

doi:10.1109/iecon.2017.8216594

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…CSA has been applied to both software [44] and hardware [52]. Following applications have benefited from CSA concepts (some of them under other terms such as adaptivity, autonomy, and goal-oriented systems): mobile applications [53], object tracking with smart cameras [24], [54], artificial intelligence [55], cloud computing [56], networks [57], operating systems [58], web [59], adaptive and dynamic compilation environment [60], Multi-Processor System-on-Chip (MPSoC) resource management [61], [62], (cyber-physical) SoC [52], mobile robots [63], industrial systems [64], [65], health monitoring [22] as well as single and multi-user active music environments [66].…”

Section: Self-adaptivenessmentioning

confidence: 99%

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

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The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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Section: Self-adaptivenessmentioning

confidence: 99%

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

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“…Also, the authors mention that several sources of information in current prognostics methods remain untapped, such as peer-to-peer evaluation and historical life-cycle information from identical assets. Insightful discussions and guidelines regarding solutions for PMS can also be found in the current literature [17,18], with some common denominators including the employment of CPPS for virtualization, ML models for data analysis (e.g. early fault detection, quality control), decentralization and self-adjustment.…”

Section: Related Workmentioning

confidence: 99%

IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0

Peres

Rocha

Leitão

et al. 2018

Computers in Industry

147

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A B S T R A C TThe manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents the guidelines for the implementation of scalable, flexible and pluggable data analysis and real-time supervision systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage through the integration of a Cyber-Physical System at the edge with cloud computing. It combines distributed data acquisition, machine learning and run-time reasoning to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.

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Concept and engineering development of cyber physical production systems: a systematic literature review

Goepp

Siadat

2020

Int J Adv Manuf Technol

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SAMBA: A self-aware health monitoring architecture for distributed industrial systems

Cited by 12 publications

References 28 publications

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0

Concept and engineering development of cyber physical production systems: a systematic literature review

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