Graph‐Based Digital Blueprint for Model Based Engineering of Complex Systems

Bajaj, Manas; Backhaus, Jonathan; Walden, Tim; Waikar, Manoj; Zwemer, Dirk; Schreiber, Chris; Issa, Ghassan F.; Martin, Lockheed

doi:10.1002/j.2334-5837.2017.00351.x

Cited by 20 publications

(12 citation statements)

References 5 publications

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“…LifeCycleInitiative.org (2022)) were not considered. Other researchers have developed systems that integrate product data from different IT systems such as PLM, ERP, and Internet of Things (IoT) into storage for metadata (Bajaj et al, 2017;Eickhoff et al, 2020;Eiden et al, 2020). Their solution integrates product data from different IT systems within a company, but it does not include data from suppliers and other external sources.…”

Section: Model-based Systems Engineering and Sustainabilitymentioning

confidence: 99%

Detection of Cause-Effect Relationships in Life Cycle Sustainability Assessment Based on an Engineering Graph

et al. 2022

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Although Life Cycle Sustainability Assessments (LCSA) are important in evaluating the sustainability of complex products and services, there is no sufficient support for engineers performing LCSA. The concept of an Engineering Graph focuses on the relations of data within engineering. It provides a model that leverages existing data in engineering and extendibility to include specialized databases and open and public data from the semantic web. This paper proposes a concept of how Engineering Graphs can be used to address the issues of LCSA and support engineers.

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Section: Model-based Systems Engineering and Sustainabilitymentioning

confidence: 99%

Detection of Cause-Effect Relationships in Life Cycle Sustainability Assessment Based on an Engineering Graph

et al. 2022

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“…However, no detailed specification of the graph model itself or data of the use case systems is provided in any of the publications, making it difficult to reproduce any of the results described in the publications. In Bajaj et al (2017) they show detailed graph query results, but fall short of providing the schema that allows to query the system. While the lack of a detailed specification may be attributed to the commercial distribution of the software, a second lack in the publications on Syndeia is a SysML modelling strategy to go with the graph schema.…”

Section: Existing Graph Schemata For Sysmlmentioning

confidence: 99%

“…While the lack of a detailed specification may be attributed to the commercial distribution of the software, a second lack in the publications on Syndeia is a SysML modelling strategy to go with the graph schema. Analogous to Petnga (2019) the queries presented by Bajaj et al (2017) on the SysML graph are of a simplistic nature and do not exploit the potential of the graph database. The queries range from which elements are connected to a specific element to Which behaviors are attributed to a specific model element and which paths exist between a specific pair of elements.…”

Section: Existing Graph Schemata For Sysmlmentioning

confidence: 99%

An Approach for System Analysis with MBSE and Graph Data Engineering

Schummer¹,

Hyba²

2022

Preprint

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Model-Based Systems Engineering aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and workpackage based on the model. Once such a model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema needs to be designed, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modelling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems. The schema and modelling approach are designed to answer questions such as what happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? Or if a condition cannot be met, which alternative routes can be established to reach a certain state of the system. We build on the use case of qualification and operations of a small spacecraft. Structural and behavioral elements of the MBSE model are transferred to a graph database where analyses are conducted on the system. The schema is implemented by an adapter for MagicDraw to Neo4j. A selection of complex analyses are shown on the example of the MOVE-II space mission. Impact StatementThe proposed schema to transfer SysML Models to Labelled Property Graphs and the related modelling strategy open a wide range of system analyses for designers and operators of complex embedded systems, such as tracking data paths, finding probable causes of anomalies or retrieving every possibility to reach a certain state within the system from the current one. It can also be used to analyze which input losses components should be resilient against to achieve a maximum level of robustness and may help modelers in choosing what information to include in their model. By parametrizing graph-queries, they can be reused for any system and thereby increase the efficiency of model analysis. The requirements imposed on the analyzed SysML model are few and do not require a holistically complete model. Thereby the approach can also be applied to system models still in the making or where parts of the system are black-boxed.

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“…Diese werden im Allgemeinen speziell für unterschiedliche Use-Cases entworfen und integrieren die in diesem fachlichen Kontext notwendigen Daten. In diesem Bereich gibt es auch eine Reihe kommerzieller Lösungen, wie Intercax Syndeia [31] oder eQube BI [32].…”

Section: Stand Der Technikunclassified

Anforderungen an ein Daten-Backend-System zur Unterstützung industrieller Datenanalyse-Anwendungen in digitalen Engineering-Prozessen dynamischer Wertschöpfungsnetzwerke

Eiden¹,

Gries²,

Eickhoff³

et al. 2020

Proceedings of the 31st Symposium Design for X (DFX2020)

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Industrial data analytics needs well-structured and linked data from different data sources. The increasing mass of data, scattered IT-structures and a lack of knowledge, especially in small and medium-sized companies (SMEs) are factors that hinder the usage of data analytics. The goal of the research project AKKORD is to build a toolkit for companies to facilitate distributed and integrated industrial data analytics inside valueadding networks. A core part of this toolkit is a data backend system, which collects and links data from different source systems together in a single meta-model. This paper describes the requirements analysis of the data backend system by conducting structured interviews and workshops.

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Graph‐Based Digital Blueprint for Model Based Engineering of Complex Systems

Cited by 20 publications

References 5 publications

Detection of Cause-Effect Relationships in Life Cycle Sustainability Assessment Based on an Engineering Graph

Detection of Cause-Effect Relationships in Life Cycle Sustainability Assessment Based on an Engineering Graph

An Approach for System Analysis with MBSE and Graph Data Engineering

Anforderungen an ein Daten-Backend-System zur Unterstützung industrieller Datenanalyse-Anwendungen in digitalen Engineering-Prozessen dynamischer Wertschöpfungsnetzwerke

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