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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2018

FUELEAP Model-Based System Safety Analysis

NASA researchers, in a partnership with Boeing, are investigating a fuel-cell powered variant of the X-57 “Maxwell” Mod-II electric propulsion aircraft, which is itself derived from a stock Tecnam P2006T. The “Fostering Ultra-Efficient Low-Emitting Aviation Power” (FUELEAP) project will replace the X-57 power subsystem with a hybrid Solid-Oxide Fuel Cell (SOFC) system to increase the potential range of the electric-propulsion aircraft while dramatically improving efficiency and emissions over stock internal-combustion engines. Our FUELEAP safety analysis faces two primary challenges. First, the Part 23 certificated Tecnam P2006T is undergoing significant modifications to host the hybrid electric-propulsion system, and the challenge is to assure that the safety inherent in the stock aircraft (and subsequently in X-57 Mod-II) is not compromised by changes in avionics, aircraft structural loading, weight and balance, or other considerations. Secondly, because the SOFC power system has little (if any) relevant in-service precedent, our challenge is to assure that we identify and mitigate all reasonably plausible hazards introduced by unique FUELEAP equipage. We are investigating and utilizing Model-Based Safety Analysis (MBSA) methods to help us address these FUELEAP safety challenges. We captured aircraft-level system hazard conditions using instances of a SysML hazard block via aircraft-level Functional Hazard Analysis (FHA). Then, using SysML models of the FUELEAP architecture, we related the hazard conditions to initiating system events and possible mitigations, such as design architecture modifications or operational constraints. We are continuing to define our approach to MBSA by developing a component-by-component inventory of local failure modes and tracing their possible contribution to hazard conditions. Finally, we are applying an argument-based approach to FUELEAP assurance. Through a FUELEAP “safety case,” we are providing an explicit argument for FUELEAP safety by associating assurance evidence with overarching safety claims through a structured argument.

Woodham, Kurt P.↗
NASA NTRS2019

Enabling Assurance in the MBSE Environment

A number of specific benefits that fit within the hallmarks of effective development are realized with implementation of model-based approaches to systems and assurance. Model Based Systems Engineering (MBSE) enabled by standardized modeling languages (e.g., SysML®) is at the core. These benefits in the context of spaceflight system challenges can include: Improved management of complex development, Reduced risk in the development process, Improved cost management, Improved design decisions. With appropriate modeling techniques the assurance community can improve early oversight and insight into project development. NASA has shown the basic constructs of SysML in an MBSE environment offer several key advantages, within a Model Based Mission Assurance (MBMA) initiative.

Evans, John W.↗
NASA NTRS2019

Realized Benefits from the Model-Based Systems Engineering Infusion and Modernization Initiative

Although Model-Based Systems Engineering (MBSE) as a concept has existed for over a decade, overall acceptance within the National Aeronautics and Space Administration (NASA) has been slow and is now growing. Since 2016, NASA’s MBSE Infusion And Modernization Initiative (MIAMI) has proven MBSE’s value to and increased its adoption at NASA. MBSE Pathfinder projects provided focused use cases that demonstrated both qualitative and quantitative benefits for systems engineering activities, and demonstrated the ability to connect MBSE models with discipline models such as structural loads and safety and mission assurance. MIAMI assisted NASA’s field centers to establish or enhance an MBSE presence. MIAMI partners with JAXA’s Systems Technology Unit to share lessons learned and demonstrate how MBSE can be used across organizations. Following its successful test cases, MIAMI is using design thinking, lean startup, and high technology marketing methodologies to implement a targeted deployment of its Community of Practice and other resources.

MBSE↗
NASA NTRSDate not supplied

Model-Based Systems Engineering, Real-Time Operations, and Autonomy

Model-Based Systems Engineering has been enabled by the development of the SysML language and software tools to create systems models. Systems models described in SysML incorporate frames (Diagrams) that represent behaviors (activities, sequences, state machines, use cases), requirements, and structure (definitions, internal structure, parametric formulation, and packaging). The SysML models are, in turn, used by applications to do analysis and studies of the designs and operational capabilities. These uses of the model are based on simulations, and do not include hardware. This paper presents a software environment and processes that enables more comprehensive systems models for MBSE, and use of these rich models for real-time operations. The paper describes a software platform that enables creation of comprehensive models, beyond what is now possible with SysML and related software tools, called the NASA Platform for Autonomous Systems (NPAS). The platform encapsulates a paradigm and infrastructure for creating systems models with complexity levels comparable to the ones handled by SysML software tools, but with additional fidelity that includes detailed design diagrams encompassing sensors, components, and design topologies. Furthermore, NPAS enables incorporation of data, information, and knowledge (DIaK) to implement autonomy and Integrated System Health Management (ISHM) and the inherent integration of content encompassing SysML structure and behavior diagrams throughout the NPAS modelAnd lastly, the NPAS models are used in real-time operations, taking advantage of the fidelity and complexity encompassed in the models in order to implement “thinking” ISHM and/or autonomous operations. . Incorporation of SysML model content into an NPAS model is briefly discussed.

MBSE↗
NASA NTRS2020

Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles

This dissertation formulates the problem of fault detection and diagnosis of DC electric power systems for the application of autonomous spacecraft. The ability to accurately identify and isolate failures in the electrical power system is critical to ensure the reliability of a spacecraft. This problem becomes more pronounced during deep space missions that lack the ability to monitor from ground control. The current state of electrical power system fault supervision is insufficient to guarantee highly reliable and robust operation. To solve this issue, a combination of model-based and rules-based techniques are used in a hierarchical framework to improve the diagnostic performance of the spacecraft electrical power system. Noise, disturbances, and modeling errors are considered in the design of the method. Practical considerations related to the hardware and software are discussed for the flight application. A wide array of failure types are simulated in a series of experiments to assess the functionality of the design. The experiments showed that the methods used improved the diagnostic capability of the autonomous system while taking into account the limitations attributed to flight software requirements. The significance of this study is to provide a framework capable of advanced diagnostics of an electrical power system with little to no interaction from a human operator.

Fault Detection and Diagnosis↗
NASA NTRS2018

The OpenSE Cookbook: A Practical, Recipe Based Collection of Patterns, Procedures, and Best Practices for Executable Systems Engineering for the Thirty Meter Telescope

The OpenSE Cookbook is an open-sourced collection of patterns, procedures, and best practices targeted for systems engineers who seek guidance on applying model-based and executable systems engineering (MBSE) using SysML. Its content has emerged from the system level modeling effort on the European Framework Program 6 (FP6) and the Thirty Meter Telescope (TMT). The TMT MBSE approach applied the Executable Systems Engineering Method (ESEM) and the open-source Engineering Environment (OpenMBEE) to specify, analyze, and verify requirements of TMT’s Alignment and Phasing System (APS) and the Narrow Field Infrared Adaptive Optics System (NFIRAOS). In these applications, implicit dependencies are made explicit in a formal model through the use of ESEM, OpenMBEE, and SysML modeling constructs. The value proposition for applying this MBSE approach was to establish precise requirements and fine-grained traceability to system designs, and to verify key requirements beginning early in development. The integration of ESEM and the OpenMBEE tooling infrastructure (providing linked-data and web-operability) is a significant added value for the MBSE approach. The APS is responsible for the overall pre-adaptive optics wavefront quality, using starlight to measure wavefront errors and align the TMT optics. In the formally integrated and executable SysML model, simulations are performed to analyze the impact of changed requirements and verify specified constraints for various operational scenarios. The APS team used several modeling patterns to capture information such as the requirements, the operational scenarios, involved subsystems and their interaction points, the estimated or required time durations, and the mass and power consumption. Adaptive optics systems are designed to sense real-time atmospheric turbulence and correct the telescope’s optical beam to remove its effect. The system model for the adaptive optics operational modes was developed to capture sequence behaviors and operational scenarios to run Monte-Carlo simulations for verifying acquisition time, observing efficiency, and operational behavior requirements. The model is particularly useful for investigating the effect of parallelization, identifying interface issues, and re-ordering sequence acquisition tasks. A former version of the Cookbook (which is now updated to MBSE challenges, goals, and lessons learned) included modeling guidelines and conventions for all system aspects, hierarchy levels, and views, which were developed during for the Active Phasing Experiment (APE), an opto-mechatronical system technology demonstrator for the Extremely Large Telescope (ELT). The Cookbook utilizes the above mentioned system models as real-world case-studies to demonstrate and document the applications of the recipes, providing also instructional examples and addressing the available tooling support. The Cookbook is accompanied by a number of SysML models and aodel libraries which facilitate model authoring and maintenance. The Cookbook covers the different aspects of Systems Engineering such as management of Requirements, Design (behavior and structure), Interfaces, Interdisciplinary Integration, Analysis, Trade Studies, and Technical Resources. This paper presents the background, motivation, architecture, and highlights some key content of the Cookbook. For example, interface management, error budget management, requirements verification, Monte Carlo driven analysis, and timing analysis of operational scenarios. The paper discusses how the capabilities of OpenMBEE contributed significantly to the adoption of executable systems engineering.

Brower, Eric↗
NASA NTRSDate not supplied

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗
NASA NTRSDate not supplied

MBSE Applications for the MSR SRC Mars Ascent Vehicle

The objective of the NASA Mars Sample Return (MSR) Campaign is to collect samples from the surface of Mars and return them to Earth for scientific research. The Mars Ascent Vehicle (MAV) will be integrated into a larger Mars Sample Retrieval Lander (SRL) for transit to and storage on Mars. After all Martian samples have been collected and loaded into the MAV payload assembly, MAV will deliver the samples from the Martian surface to Mars orbit. A separate spacecraft, the Earth Return Orbiter (ERO) will retrieve the samples from Mars orbit and return them to Earth. To address common systems engineering challenges associated with using traditional systems engineering practices on complex projects, the MAV systems engineering team has explored implementation of Model-Based Systems Engineering (MBSE) tools and languages. This paper describes the current state of implementation and development of the MAV MBSE model with the Systems Modeling Language (SysML) within the scope of the MAV Systems Requirement Cycle (SRC) systems engineering workflow. The MAV MBSE model has been developed within Magic Draw – a SysML editor commonly used to implement MBSE. The MAV MBSE model has been used to develop mission phase functional flow diagrams for the Concept of Operations, decompose mission to vehicle subsystem functions, develop a functional decomposition, derive functional requirements, trace requirements up to customer-imposed requirements, trace requirements within MAV requirement space, identify requirements trace gaps, define and map the physical design space architecture, allocate requirements to subsystems, develop validation items, define assembly, integration, and test (AI&T) operations, and trace these items across driving goals to develop an integrated digital thread of systems engineering information used to drive design specifications, decision making, and ultimately design verification and validation. Findings and results associated with implementing MBSE in these ways, alongside traditional methods will be discussed.

MBSE↗
NASA NTRS2020

Model Checking as a Service: Towards Pragmatic Hidden Formal Methods

Executable models can be used to support all engineering activities in Model-Based Systems Engineering. Testing and simulation of such models can provide early feedback about design choices. How-ever, in today’s complex systems failures could arise due to subtle errors that are hard to find without checking all possible execution paths. Formal methods, and especially model checking can uncover such subtle errors, yet their usage in practice is limited due to the specialized expertise and high computing power required. There-fore we created an automated, cloud-based environment that can verify complex reachability properties on SysML State Machines using hidden model checkers. The approach and the prototype is illustrated using an example from the aerospace domain.

Karban, Robert↗
NASA NTRS2020

ROMAN CGI Testbed WFSC Modeling and Validation

Optical diffraction and wavefront sensing and control (WFSC) models validated against the high-fidelity Roman Space Telescope Coronagraph Instrument (CGI) testbed play a key role in mask design selection and the verification of many requirements that cannot be accomplished until the observatory is in orbit. We have been steadily improving our model fidelity for the as-built CGI testbed system, demonstrating recently good agreement between measurements and model predictions while validating the Hybrid Lyot Coronagraph’s (HLC) performance using the in-orbit high order wavefront sensing and control (HOWFSC) operational scenario. We present modeling and testbed validation results that explain the reason many testbed WFSC iterations were needed for HLC in the past. A new, direct application of model-generated deformable mirror (DM) solutions has since been successfully demonstrated on the testbed with significant speed and performance improvement. The benefit of using such a solution opens up new model-based WFSC approaches for CGI. This can greatly reduce flight risk from potentially insufficient ground solution generation due to schedule or cost constraints or from unexpected post-delivery changes.

Poberezhskiy, Ilya↗
NASA NTRSDate not supplied

MBSE Applications for the MSR SRC Mars Ascent Vehicle

The objective of the NASA Mars Sample Return (MSR) Campaign is to collect samples from the surface of Mars and return them to Earth for scientific research. The Mars Ascent Vehicle (MAV) will be integrated into a larger Mars Sample Retrieval Lander (SRL) for transit to and storage on Mars. After all Martian samples have been collected and loaded into the MAV payload assembly, MAV will deliver the samples from the Martian surface to Mars orbit. A separate spacecraft, the Earth Return Orbiter (ERO) will retrieve the samples from Mars orbit and return them to Earth. To address common systems engineering challenges associated with using traditional systems engineering practices on complex projects, the MAV systems engineering team has explored implementation of Model-Based Systems Engineering (MBSE) tools and languages. This paper describes the current state of implementation and development of the MAV MBSE model with the Systems Modeling Language (SysML) within the scope of the MAV Systems Requirement Cycle (SRC) systems engineering workflow. The MAV MBSE model has been developed within Magic Draw – a SysML editor commonly used to implement MBSE. The MAV MBSE model has been used to develop mission phase functional flow diagrams for the Concept of Operations, decompose mission to vehicle subsystem functions, develop a functional decomposition, derive functional requirements, trace requirements up to customer-imposed requirements, trace requirements within MAV requirement space, identify requirements trace gaps, define and map the physical design space architecture, allocate requirements to subsystems, develop validation items, define assembly, integration, and test (AI&T) operations, and trace these items across driving goals to develop an integrated digital thread of systems engineering information used to drive design specifications, decision making, and ultimately design verification and validation. Findings and results associated with implementing MBSE in these ways, alongside traditional methods will be discussed.

MBSE↗
NASA NTRS2022

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems The National Academies have prioritized the pursuit of new scientific discoveries using diverse and temporally coordinated measurements from multiple ground and space-based observatories. Networked communications can enable such measurements by connecting individual observatories and allowing them to operate as a cohesive and purposefully designed system. Timely data flows across terrestrial and space communications networks are required to observe transient scientific events and processes. Currently, communications to space-based observatories experience large latencies due to manual service reservation and scheduling procedures, intermittent signal coverage, and network capacity constraints. If space communications network latencies could be reduced, new discoveries about dynamic scientific processes could be realized. However, science mission and network planners lack a systematic framework for defining, quantifying and evaluating timely space data flow implementation options for transient scientific observation scenarios involving multiple ground and space-based observatories. This dissertation presents a model-based systems engineering approach to investigate and develop network solutions to meet the needs of transient science space systems. First, a systematic investigation of the current transient science operations of the National Aeronautics and Space Administration’s (NASA) Tracking and Data Relay Satellite (TDRS) space data network and the Neil Gehrels Swift Observatory resulted in a formal architectural model for transient science space systems. Two methods individual missions may use to achieve timely network services were defined, quantitatively modeled, and experimentally compared. Next, the architectural model was extended to describe two alternative ways to achieve timely and autonomous space data flows to multiple space-based observatories within the context of a purposefully designed transient science observation scenario. A quantitative multipoint space data flow modeling method based in queueing theory was defined. General system suitability metrics for timeliness, throughput, and capacity were specified to support the evaluation of alternative network data flow implementations. A hypothetical design study was performed to demonstrate the multipoint data flow modeling method and to evaluate alternative data flow implementations using TDRS. The merits of a proposed future TDRS broadcast service to implement multipoint data flows were quantified and compared to expected outcomes using the as-built TDRS network. Then, the architectural model was extended to incorporate commercial network service providers. Quantitative models for Globalstar and Iridium short messaging data services were developed based on publicly available sources. Financial cost was added to the set of system suitability metrics. The hypothetical design study was extended to compare the relative suitability of the as-built TDRS network with the commercial Globalstar and Iridium networks. Finally, results from this research are being applied by NASA missions and network planners. In 2020, Swift implemented the first automated command pipeline, increasing its expected gravitational wave follow-up detection rate by greater than 400%. Current NASA technology initiatives informed by this research will enable future space-based observatories to become interoperable sensing devices connected by a diverse ecosystem of network service providers.

Christopher J. Roberts↗
NASA NTRS2022

A Systems Model for System-Wide Safety Safety Demonstrator (SD-1): Wildfire Response Operations

The aim of this internship-based project was to contribute to the ongoing development of a systems model for System-Wide Safety’s first Technical Challenge 5 (TC5) series Safety Demonstrator (SD-1), which will be a demonstration of an In-Time Aviation Safety Management System (IASMS) in emerging wildfire response operations. Using Models-Based Systems Engineering (MBSE) principles to develop the model, I organized and traced previously collected stakeholder needs from the Spring 2022 NASA System-Wide Safety Wildland Firefighting Operations Virtual Workshop (https://nari.arc.nasa.gov/sws-wildfire) to system elements, creating connections which can be used in the future by the project engineers to identify and address requirements gaps throughout the system design process. I also identified and modeled preliminary use case scenarios for aerial assets in the demonstrator and, building on previously produced preliminary high-level models of the 8 SD-1 Services, Functions, and Capabilities (SFCs) and their IASMS data flows, worked to model the Real-Time Risk Assessment (RTRA) tool as an implementation of Risk Assessment and Management that can take in multiple sets of data monitored by SFCs. Project deliverables include stakeholder requirements tables and matrices and systems model diagrams produced with MagicDraw software in the SysML Systems Modeling Language, with eventual plans to connect model diagrams to a Department of Defense Operational Viewpoint (OV-1) graphic, a high-level operational concept graphic that will be used to visualize the SD-1 scenarioin a future phase. The system model serves to provide a common understanding of the scope of and activities necessary for the completion of SD-1,and traces how stakeholder needs are to be addressed.

model-based systems engineering↗
NASA NTRS2021

IceNode: A Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements Under Ice Shelves

Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean and significantly raising global sea level. However, the accelerating dynamics of ice shelf melt in a warming environment are poorly understood, and the collapse of Antarctic ice shelves remains one of the largest sources of uncertainty in global sea level rise projections. The cavities below Antarctic ice shelves are notoriously difficult to access, making model-based hypotheses about the relationship between ocean warming and greater ice shelf melting difficult to verify because of a lack of in-situ data to constrain model parameters and examine key assumptions. We present early progress on IceNode, a novel vehicle under development at the NASA Jet Propulsion Laboratory designed to acquire well-distributed, concurrent, long-duration melt rate measurements under ice shelves. IceNodes are deployed as an array from a ship at the shelf edge, and use variable buoyancy to ride melt-driven exchange currents far into the cavity. Once underneath their target, they release a ballast weight to become highly positively buoyant and attach to the underside of the ice shelf, where they acquire in-situ measurements of basal melt rate directly at the ice-ocean interface for a year or more. Finally, IceNodes detach from their landing structure and use variable buoyancy to ride melt-driven exchange currents back to open water, where they surface and transmit their mission data home. IceNodes are designed to be relatively low-cost, expendable, and have simple logistics, enabling scientists to deploy scalable arrays that simultaneously measure co-varying ice shelf melt and ocean conditions over large spatial areas, thereby providing an unprecedented view of ice shelf melt rate variability and its drivers.

Zapien, Xavier↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

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↗
DOE OSTI2025

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗