Semantic Asset Administration Shells in Industry 4.0: A Survey

Beden, Sadeer; Cao, Qiushi; Beckmann, Arnold

doi:10.1109/icps49255.2021.9468266

Cited by 14 publications

(4 citation statements)

References 34 publications

(69 reference statements)

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“…For examples, the physical decomposition is differently expressed by several standards, like the ISO 14224, ISO 15926, and IEC 62264, used to develop AAS (Asset Administration Shell). However, it is already demonstrated that the maintenance-related terms urge effort to be syntactically and semantically consistent [36,49]. Thus, MOCA extensively relies on BFO-compliant ontologies, and, in case of missing or contradicting formalisations, a look to international standards is given; ultimately, a look towards the vocabulary already used by the food company in which the ontology is verified is given to resolve incompliances.…”

Section: Ontology Development Methodology and Design Choicesmentioning

confidence: 99%

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Polenghi

Roda

Macchi

et al. 2022

Auton. Intell. Syst.

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Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.

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Section: Ontology Development Methodology and Design Choicesmentioning

confidence: 99%

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Polenghi

Roda

Macchi

et al. 2022

Auton. Intell. Syst.

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“…In [63], the authors design and propose an AAS model for predictive maintenance. Moreover, in [64], the authors provided a comprehensive survey of AAS models for the information and communication layer, where they presented some ontologies used for scheduling, i.e., see [65]. Also, in [66], the authors proposed a modeling approach that was AAS-based using ontologies to overcome the gap of semantic interoperability and allow efficient interactions between Industry 4.0.…”

Section: Related Workmentioning

confidence: 99%

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

Siatras,

Bakopoulos,

Mavrothalassitis

et al. 2023

Applied Sciences

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Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system’s ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent’s information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent’s ability to adapt and provide fast and accurate scheduling results.

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“…As a result, one research direction shifted towards ontology development to capture domain knowledge and concepts to achieve this goal. Previously, we surveyed the scientific contributions of semantic AASs that used ontologies to model the Information and Communication layers of RAMI 4.0 in [12]. In that paper, we summarized the use cases and technologies used to develop different semantic AASs for the overall goal of improved data semantics and interopability within Industry 4.0.…”

Section: Ontologies For Industry 40mentioning

confidence: 99%

SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

2021

Self Cite

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This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel.

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Semantic Asset Administration Shells in Industry 4.0: A Survey

Cited by 14 publications

References 34 publications

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0

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