Digital Availability of Product Information for Collaborative Engineering of Spacecraft

In this paper, we introduce a system to collect product information from manufacturers and make it available in tools that are used for concurrent design of spacecraft. The planning of a spacecraft needs experts from different disciplines, like propulsion, power, and thermal. Since these different disciplines rely on each other there is a high need for communication between them, which is often realized by a Model-Based Systems Engineering (MBSE) process and corresponding tools. We show by comparison that the product information provided by manufacturers often does not match the information needed by MBSE tools on a syntactic or semantic level. The information from manufacturers is also currently not available in machine-readable formats. Afterwards, we present a prototype of a system that makes product information from manufacturers directly available in MBSE tools, in a machine-readable way.Comment: accepted at CDVE201

Decentralized and automated business logic in a future MBSE blockchain ecosystem

Blockchain represents a ready-to-use framework to distribute information, record changes and agree, by consensus, upon the global state of represented things. Blockchain systems can be equipped with a layer that can model the business logic in a pre-programmed and automated way. This is achieved through the use of smart contracts, that are computer programs stored on a blockchain and visible to all participants. Smart contracts represent digital assets that can be triggered upon met pre-conditions. They can be used to represent the various items of the MBSE domain such as requirements, system engineers, missions, spacecrafts and subsystems. Creating digital twins of these components along with their representation as assets in the blockchain enables multitude of use cases for flexible automation and integration in various workflows. This papers illustrate an innovation prototype in implementation; Exochain, a blockchain approach to MBSE

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE

MBSE METHODOLOGY AND ANALYSIS TOOL TO IMPLEMENT MBSE POST MILESTONE C

This thesis proposes a model-based systems engineering (MBSE) methodology to be implemented post Milestone C, develops a Microsoft Excel MBSE analysis tool which provides a recommendation to implement MBSE, and provides a case study for implementing MBSE post Milestone C on a Department of Defense (DoD) acquisition program. The purpose of the MBSE methodology is to identify how MBSE should be implemented post Milestone C to address the systemic challenges which are faced by DoD acquisition programs post Milestone C. The Excel MBSE analysis tool provides a set of questions which provide metrics to the program office to determine the benefit of implementing MBSE post Milestone C into their program. The thesis then details, through a case study, how the Excel MBSE analysis tool can be used to decide whether to implement MBSE. Prior research on the systemic challenges within DoD acquisition programs as well as the use of MBSE during post Milestone C activities were leveraged in developing the proposed MBSE methodology and Excel MBSE analysis tool. The thesis makes a recommendation to implement MBSE post Milestone C to mitigate schedule, cost, and risk uncertainties. This is done through digitally linking various models, such as a manufacturing model and a logistics model to an integrated master schedule (IMS). Based on the metrics and cost, the Excel MBSE analysis tool provides a recommendation on which models should be implemented.http://archive.org/details/mbsemethodologya1094560439Civilian, Department of the NavyApproved for public release; distribution is unlimited

Process Productivity Improvements through Semantic and Linked Data Technologies

Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Álvarez Rodríguez.- Secretario: Rafael Valencia García.- Vocal: Alejandro Rodríguez Gonzále

Supporting Early Mission Concept Evaluation through Natural Language Processing

Proposal evaluation of pre-Phase A mission concepts is largely based on the input from subject matter experts who determine the scientific merit of a mission concept based on a number of criteria including: the relevance of the mission objectives to national and international priorities; the existence of a complete set of measurement, instrument, and platform requirements that are traceable to the mission objectives; and several others. The Science Traceability Matrix is a standard tool used to articulate this relevance and traceability and therefore is a key input to this reviewing process. However, inconsistencies in the structure and vocabulary used in the Science Traceability Matrix and other sections of the proposal across organizations make this process challenging and time-consuming. At the same time, as part of the Digital Engineering revolution, NASA and other space organizations are starting to embrace key concepts of model-based systems engineering and understand the value of moving from unstructured text documents to more formal knowledge representations that are amenable to automated data processing. In this line, this thesis leverages transformer models, a recent advance in natural language processing, to demonstrate automatic extraction of science relevance and traceability information from unstructured mission concept proposals. By doing so, this work helps pave the way for future applications of natural language processing to support other systems engineering practices within mission/program development such as automated parsing of design documentation. The proposed tool, called AstroNLP, is evaluated with a case study based on the Astrophysics Decadal Survey

A Graph Transformation Method for Robotic Satellite Servicing Down-Selection

As remote robotic space satellite servicing technologies develop, each servicer satellite will need to account for a number of servicing scenarios and consider a variety of alternate design solutions to best meet the most servicing scenario requirements. This thesis presents a graph transformation method for systematically down-selecting the number of design options available, and highlighting trade-offs in sets of design solutions which best meet satellite servicing task requirements while also reducing total mass, maximum power needed and servicing time. The proposed method successfully identifies for further consideration several best design solutions from a set of approximately 10,000 potential solutions in the first test case examined, and from a set of approximately 2*1026 in the second test case examined

A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories

From Data Modeling to Knowledge Engineering in Space System Design

The technologies currently employed for modeling complex systems, such as aircraft, spacecraft, or infrastructures, are sufficient for system description, but do not allow deriving knowledge about the modeled systems. This work provides the means to describe space systems in a way that allows automating activities such as deriving knowledge about critical parts of the system’s design, evaluation of test success, and identification of single points of failure