Towards Implementing Artificial Thinking Autonomy with Model-Based Systems Engineering
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Reliability Analysis of Complex NASA Systems with Model-Based Engineering
The emergence of model-based engineering, with Model- Based Systems Engineering (MBSE) leading the way, is transforming design and analysis methodologies. The recognized benefits to systems development include moving from document-centric information systems and document-centric project communication to a model-centric environment in which control of design changes in the life cycles is facilitated. In addition, a “single source of truth” about the system, that is up-to-date in all respects of the design, becomes the authoritative source of data and information about the system. This promotes consistency and efficiency in regard to integration of the system elements as the design emerges and thereby may further optimize the design. Therefore Reliability Engineers (REs) supporting NASA missions must be integrated into model-based engineering to ensure the outputs of their analyses are relevant and value-needed to the design, development, and operational processes for failure risks assessment and communication.
FMEA/FMECA↗
Model Based Document and Report Generation for Systems Engineering
As Model Based Systems Engineering (MBSE) practices gain adoption, various approaches have been developed in order to simplify and automate the process of generating documents from models. Essentially, all of these techniques can be unified around the concept of producing different views of the model according to the needs of the intended audience. In this paper, we will describe a technique developed at JPL of applying SysML Viewpoints and Views to generate documents and reports. An architecture of model-based view and document generation will be presented, and the necessary extensions to SysML with associated rationale will be explained. A survey of examples will highlight a variety of views that can be generated, and will provide some insight into how collaboration and integration is enabled. We will also describe the basic architecture for the enterprise applications that support this approach.
Architecture↗
Bringing Back the Social Affordances of the Paper Memo to Aerospace Systems Engineering Work
Model-based systems engineering (MBSE) is a relatively new field that brings together the interdisciplinary study of technological components of a project (systems engineering) with a model-based ontology to express the hierarchical and behavioral relationships between the components (computational modeling). Despite the compelling promises of the benefits of MBSE, such as improved communication and productivity due to an underlying language and data model, we observed hesitation to its adoption at the NASA Jet Propulsion Laboratory. To investigate, we conducted a six-month ethnographic field investigation and needs validation with 19 systems engineers. This paper contributes our observations of a generational shift in one of JPL's core technologies. We report on a cultural misunderstanding between communities of practice that bolsters the existing technology drag. Given the high cost of failure, we springboard our observations into a design hypothesis - an intervention that blends the social affordances of the narrative-based work flow with the rich technological advantages of explicit data references and relationships of the model-based approach. We provide a design rationale, and the results of our evaluation.
design↗
Model-Based Systems
Engineers, who design systems using text specification documents, focus their work upon the completed system to meet Performance, time and budget goals. Consistency and integrity is difficult to maintain within text documents for a single complex system and more difficult to maintain as several systems are combined into higher-level systems, are maintained over decades, and evolve technically and in performance through updates. This system design approach frequently results in major changes during the system integration and test phase, and in time and budget overruns. Engineers who build system specification documents within a model-based systems environment go a step further and aggregate all of the data. They interrelate all of the data to insure consistency and integrity. After the model is constructed, the various system specification documents are prepared, all from the same database. The consistency and integrity of the model is assured, therefore the consistency and integrity of the various specification documents is insured. This article attempts to define model-based systems relative to such an environment. The intent is to expose the complexity of the enabling problem by outlining what is needed, why it is needed and how needs are being addressed by international standards writing teams.
Frisch, Harold P.↗
System Safety Analysis of Complex NASA Systems with Model Based Engineering
The emergence of model-based engineering is transforming design and analysis methodologies [5]. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses [1], GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System
Model Based Engineering↗
Architectural Framework for Conceptualizing Exploration Class Medical Operations
Currently, discrete projects within NASA’s Human Research Program (HRP) take aim at providing future exploration missions with the capabilities necessary to operate within a progressively Earth-independent operative environment. Realizing an entire ecosystem that can accomplish these goals requires a clear visualization on how each project contributes to the overall objectives of Human Health and Performance (HH&P) in that context. The Exploration Medical Capability (ExMC) Element has developed an architectural framework for exploration class medical operations by utilizing a systems engineering approach using model-based systems engineering (MBSE). This model, a System of Systems Architecture (SoSA), captures a means of integrating various independent but related efforts into a comprehensive view associated with HRP’s goal of providing progressively Earth-Independent Medical Operations (EIMO). This level of visibility is realized by identifying interfaces between development efforts and existing programs that contribute to the overall mission of EIMO even if those extend past the traditional boundary of HRP. Potential interfaces that are considered important when constructing an architecture include program/project Needs, Goals, and Objectives (NGOs), capabilities, envisioned use cases/scenarios, and system/subsystem structure. This presentation will focus on the processes that were used to generate the SoSA along with a means to realize its utility amongst other MBSE products from ExMC. Evaluation of the best architecture moving forward can then be achieved that promotes cohesion between various functional areas of HH&P. Additionally, the SoSA is developed with a long-term vision for the dynamic integration of novel technological developments that may be implemented in the future. The SoSA is intended to enable Stakeholders the ability to view the broader interconnections that exist and begin to create avenues of communication between various efforts. The benefits of a SoSA provide an essential step towards addressing the unique challenges of attaining the goals of broader exploration class medical missions of the future.
Systems engineering↗
Architectural Framework for Conceptualizing Exploration Class Medical Operations
Currently, discrete projects within NASA’s Human Research Program (HRP) take aim at providing future exploration missions with the capabilities necessary to operate within a progressively Earth-independent operative environment. Realizing an entire ecosystem that can accomplish these goals requires a clear visualization on how each project contributes to the overall objectives of Human Health and Performance (HH&P) in that context. The Exploration Medical Capability (ExMC) Element has developed an architectural framework for exploration class medical operations by utilizing a systems engineering approach using model-based systems engineering (MBSE). This model, a System of Systems Architecture (SoSA), captures a means of integrating various independent but related efforts into a comprehensive view associated with HRP’s goal of providing progressively Earth-Independent Medical Operations (EIMO). This level of visibility is realized by identifying interfaces between development efforts and existing programs that contribute to the overall mission of EIMO even if those extend past the traditional boundary of HRP. Potential interfaces that are considered important when constructing an architecture include program/project Needs, Goals, and Objectives (NGOs), capabilities, envisioned use cases/scenarios, and system/subsystem structure. This presentation will focus on the processes that were used to generate the SoSA along with a means to realize its utility amongst other MBSE products from ExMC. Evaluation of the best architecture moving forward can then be achieved that promotes cohesion between various functional areas of HH&P. Additionally, the SoSA is developed with a long-term vision for the dynamic integration of novel technological developments that may be implemented in the future. The SoSA is intended to enable Stakeholders the ability to view the broader interconnections that exist and begin to create avenues of communication between various efforts. The benefits of a SoSA provide an essential step towards addressing the unique challenges of attaining the goals of broader exploration class medical missions of the future.
Systems engineering↗
Introduction to an MBSE Case Study
During this interactive activity, we will review real-world challenges decision-makers have faced and work together in small groups to develop solutions to those challenges by applying the principles of model-based systems engineering (MBSE).
systems engineering↗
Medical System Foundation Overview for Long-Duration Lunar Orbit and Surface Operations Missions
The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a Medical System Foundation for Level of Care IV, as defined by NASA’s space flight human-system standards, for long-duration lunar orbit and surface operation missions by employing a systems engineering approach using model-based systems engineering tools. This Foundation model includes a concept of operations; functional decomposition; clinical content (medical conditions, capabilities, and resources); associated functional, interface and non-functional technical requirements; and traces to the current versions of NASA standards documents and parent-level (Program- and Vehicle habitat system level) requirements. Collectively, these components constitute a foundation that serves as a starting point for a medical system that meets the Level of Care IV requirement. The Foundation was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA, and information is presented in an easily accessible format that is understandable across disciplines. Stakeholders can use the Foundation to analyze the traces between medical capabilities, medical conditions, medical resources, and requirements and to identify medical system interfaces with other vehicle systems/subsystems. It can also be used as a basis for performing trades on risks vs. medical system mass and volume allocation. This discussion will focus on the processes through which the Medical System Foundation was developed, how the Foundation builds a bridge between the medical and engineering domains and facilitates communication between these communities, and how these processes can be applied more broadly to a crew health and performance system and other system domains. The presentation also discusses Foundation modifications based on the recently updated versions of the NASA 3001 Standards.
S. Lumpkins↗
NASA Space Nuclear Propulsion (SNP) MBSE Initiatives
NASA’s Space Nuclear Propulsion (SNP) program is developing several MagicDraw SysML models to support the development of high performance Nuclear Thermal Rocket Engines (NTRE). Currently, the Demonstration Rocket for Agile Cislunar Operations (DRACO) project is aiming to perform the first ever flight demonstration of an NTRE, and NASA is developing a DRACO Insight Project Model Based Systems Engineering (MBSE) model to capture, define, analyze, and report on the flight and ground test system architecture, functional behavior, requirements, risks, and lessons learned. Additional models are in work for engine component trade trees, fault detection sensor coverage analysis using a Goal Function Tree (GFT) plugin, stakeholder engagement, and technology maturation projects. The GFT plugin is the Galois, Inc. Failure Recovery Instruction Generation using Automata derived from Traditional Engineering models (FRIGATE) tool. A new capability for Jira to MagicDraw data sharing using the OpenPDM collaboration platform is under development with partner Victory Solutions, Inc. to enhance risk impact analysis.
Space Nuclear Propulsion (SNP)↗
Implementation of a Goal-Based Systems Engineering Process Using the Systems Modeling Language (SysML)
Building upon the purpose, theoretical approach, and use of a Goal-Function Tree (GFT) being presented by Dr. Stephen B. Johnson, described in a related Infotech 2013 ISHM abstract titled "Goal-Function Tree Modeling for Systems Engineering and Fault Management", this paper will describe the core framework used to implement the GFTbased systems engineering process using the Systems Modeling Language (SysML). These two papers are ideally accepted and presented together in the same Infotech session. Statement of problem: SysML, as a tool, is currently not capable of implementing the theoretical approach described within the "Goal-Function Tree Modeling for Systems Engineering and Fault Management" paper cited above. More generally, SysML's current capabilities to model functional decompositions in the rigorous manner required in the GFT approach are limited. The GFT is a new Model-Based Systems Engineering (MBSE) approach to the development of goals and requirements, functions, and its linkage to design. As a growing standard for systems engineering, it is important to develop methods to implement GFT in SysML. Proposed Method of Solution: Many of the central concepts of the SysML language are needed to implement a GFT for large complex systems. In the implementation of those central concepts, the following will be described in detail: changes to the nominal SysML process, model view definitions and examples, diagram definitions and examples, and detailed SysML construct and stereotype definitions.
Patterson, Jonathan D.↗
Implementation of a Goal-Based Systems Engineering Process Using the Systems Modeling Language (SysML)
Building upon the purpose, theoretical approach, and use of a Goal-Function Tree (GFT) being presented by Dr. Stephen B. Johnson, described in a related Infotech 2013 ISHM abstract titled "Goal-Function Tree Modeling for Systems Engineering and Fault Management", this paper will describe the core framework used to implement the GFTbased systems engineering process using the Systems Modeling Language (SysML). These two papers are ideally accepted and presented together in the same Infotech session. Statement of problem: SysML, as a tool, is currently not capable of implementing the theoretical approach described within the "Goal-Function Tree Modeling for Systems Engineering and Fault Management" paper cited above. More generally, SysML's current capabilities to model functional decompositions in the rigorous manner required in the GFT approach are limited. The GFT is a new Model-Based Systems Engineering (MBSE) approach to the development of goals and requirements, functions, and its linkage to design. As a growing standard for systems engineering, it is important to develop methods to implement GFT in SysML. Proposed Method of Solution: Many of the central concepts of the SysML language are needed to implement a GFT for large complex systems. In the implementation of those central concepts, the following will be described in detail: changes to the nominal SysML process, model view definitions and examples, diagram definitions and examples, and detailed SysML construct and stereotype definitions.
Breckenridge, Jonathan T.↗
Towards a Reference Architecture for Model-Based Engineering Environments
A key aspect of adopting model-based systems engineering as a practice in an organization is the design and development, and adoption of corresponding processes and tools that support the model-based paradigm. In an effort to enable the unified implementation of such processes and tools, this paper introduces a reference architecture model that serves as a specification for a model-based engineering environment. Current systems engineering practices, products, processes and technologies are used as input for continuously refining the architecture model. In the paper, we introduce and report on the current status of this reference architecture model, and present the methodology applied in developing the reference architecture. We conclude that while there are a very large number of domain- or application-specific processes requiring specialized behavior, these can be reduced through abstraction to a small set of core functions that need to be supported by a realization of a model-based engineering environment. Only very few organization-domain- or application-specific aspects require specialized consideration.
Herzig, Sebastian J. I.↗
System Engineers and Decisions: It?s All about Knowledge
In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).
97 - MATHEMATICS AND COMPUTING↗
A Knowledge Graph Approach to Analyze Systems and Assets Health
Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.
97 - MATHEMATICS AND COMPUTING↗
From Data to Knowledge: A Graph-Based Reliability Approach to Assess System Health
With the goal of maximizing plant reliability and availability, complex systems such as nuclear power plants continuously monitor and record the performance and the health status of many components, assets, and systems. Such data may take the form of online monitoring data, condition reports, and maintenance reports and it carries the potential to provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind them and to predict their direct consequences. The analysis of such data poses however few challenges. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly tackles these challenges, and it focuses on the integration of all these data elements in order to assist plant system engineers in analyzing component, assets, and systems performances and optimize maintenance activities. This is performed by 1) extracting knowledge from textual data via technical language processing methods, and 2) quantifying system, asset, and component health from numeric condition-based data. We rely on model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Numeric and textual data elements are then associated with an MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.
97 MATHEMATICS AND COMPUTING↗
Model Based Mission Assurance: Emerging Opportunities for Robotic Systems
The emergence of Model Based Systems Engineering (MBSE) in a Model Based Engineering framework has created new opportunities to improve effectiveness and efficiencies across the assurance functions. The MBSE environment supports not only system architecture development, but provides for support of Systems Safety, Reliability and Risk Analysis concurrently in the same framework. Linking to detailed design will further improve assurance capabilities to support failures avoidance and mitigation in flight systems. This also is leading new assurance functions including model assurance and management of uncertainty in the modeling environment. Further, the assurance cases, a structured hierarchal argument or model, are emerging as a basis for supporting a comprehensive viewpoint in which to support Model Based Mission Assurance (MBMA).
Mission Assurance↗