A Semantic Approach to Spacecraft Verification Planning Using Bayesian Networks

Gregory, Joe; Salado, Alejandro

doi:10.1109/aero58975.2024.10521072

Cited by 4 publications

References 28 publications

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The Digital Engineering Factory: Considerations, Current Status, and Lessons Learned

Gregory,

Salado,

O'Neal

et al. 2024

INCOSE International Symp

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In industry, the advancement of digital engineering and the digital thread aims to reduce the impact of knowledge ‘siloes’ by providing a way to integrate data across the entire system lifecycle and across multiple domains. In a typical engineering curriculum, however, courses are still treated as ‘siloes’, and students often do not have the opportunity to experience this industrially relevant approach to engineering. The Digital Engineering Factory (DEF) is a digital engineering environment under development at the University of Arizona to support engineering students. The DEF supports students by providing access to multiple engineering tools and is structured using a ‘hub‐and‐spoke’ approach to consolidate data from these tools. Through this connected architecture, students can transfer data generated in a particular course to tools for use in other courses. Connecting course activities in this way enables students to experience a complete end‐to‐end system lifecycle. At its ‘hub’, the DEF uses Violet to integrate data from multiple sources, create a digital thread, and generate a graph representation of the dataset. This knowledge graph, written in the Ontological Modeling Language (OML), can be viewed in OML Rosetta and is structured according to the University of Arizona Ontology Stack (UAOS). The use of the UAOS and OML Rosetta allows instructors to leverage semantic web technologies to support teaching activities such as grading. In this paper, the authors review the objectives of the DEF, discuss the status of the project, and highlight current limitations and lessons learned with regards to its deployment. These may be useful to inform similar developments in industrial settings.

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The Digital Engineering Factory: Considerations, Current Status, and Lessons Learned

Gregory,

Salado,

O'Neal

et al. 2024

INCOSE International Symp

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Towards a Systems Engineering Ontology Stack

Gregory,

Salado

2024

INCOSE International Symp

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Semantic Web Technologies (SWTs) provide an approach to the structuring and understanding of data. SWTs utilize ontologies, reasoners, and query languages to structure existing knowledge, validate knowledge, and infer new knowledge. Ontologies in particular play a central role in enabling reusability and interoperability between domains. A common way to organize ontologies and their dependencies is in a layered ontology stack. These layers often incorporate top‐level, core and domain ontologies. Libraries of standard instances can also be used. Federating the conceptualization of a domain across upper‐ and lower‐level ontologies improves the reusability of higher‐level terminology in other domains, and therefore improves interoperability between them. The University of Arizona Ontology Stack (UAOS) is a layered, modular ontology stack that has been developed to support digital engineering activities at the University of Arizona. It is based on the Basic Formal Ontology (BFO), and currently comprises five core ontologies and 12 domain ontologies. The UAOS reuses existing ontologies and standards wherever possible. The core ontologies, for example, are based on the Common Core Ontologies, developed at CUBRC, and the Provenance Notation (PROV‐N), a W3C standard. Domain ontologies include the System Architecture Ontology, based on ISO 42010, and the Orbits and Trajectories Ontology, based on CUBRC's Space Object Ontology. In this paper, we report on the development of the UAOS, present examples of how it has been used to support digital engineering research, discuss the challenges of integrating ontologies from multiple sources into a cohesive stack, and highlight topics of interest for future research.

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An ontology‐based digital test and evaluation master plan (dTEMP) compliant with DoD policy

Gregory,

Salado

2024

Systems Engineering

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Test and evaluation (TE) planning is a critical part of systems engineering. However, it has not received as much attention from digital engineering efforts as early‐stage design and analysis. Digital engineering has the potential to reduce the risk and effort associated with TE, while leveraging existing digital capabilities to add value. One aspect in particular that may benefit from such attention is the Department of Defense's Test and Evaluation Master Plan (TEMP). The purpose of the TEMP is to identify the key processes with respect to the TE of a product, and to specify the roles and responsibilities of key personnel and organizations. Concerns have been raised regarding the document‐based nature of the TEMP and the increased risk and reduced reward that this entails. In this paper, we investigate the potential benefits of digitalizing the TEMP and outline an incremental approach for achieving this. We also present a set of ontologies, collectively known as the Digital TEMP (dTEMP), and investigate potential benefits and limitations by applying the dTEMP to an example test program.

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A Semantic Approach to Spacecraft Verification Planning Using Bayesian Networks

Cited by 4 publications

References 28 publications

The Digital Engineering Factory: Considerations, Current Status, and Lessons Learned

The Digital Engineering Factory: Considerations, Current Status, and Lessons Learned

Towards a Systems Engineering Ontology Stack

An ontology‐based digital test and evaluation master plan (dTEMP) compliant with DoD policy

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