ROSE MIU Testing

Overview of work done by Rebekah Austin during Pathways Internship work tour. Describes ROSE MIU (Reconfigurable Operational Spacecraft for Science and Exploration Module Interface Unit) features and test plan

Model-Based Assurance for Satellites with Commercial Parts in Radiation Environments

Small satellite projects often do not have the budget or schedule to incorporate radiation-hardened parts or extensive radiation test campaigns into their schedule. Yet a case must be made that the spacecraft will function as intended in orbit, with radiation, temperature and vacuum affecting part performance. The Vanderbilt Institute for Space and Defense Electronics, with support from NASA HQ, NASA NEPP, and NASA JPL, has developed a platform for making a safety case for systems with commercial (non-hardened) parts, called the Systems Engineering Assurance and Modeling (SEAM) platform. The platform has three elements: goal structuring notation (GSN), systems engineering models (SysML and our extensions), and Bayesian networks (BN). The GSN is a visual argument structure that presents an argument that the system meets specifications based on goals, strategies, and evidence. The systems engineering model is a high-level descriptive language that captures the spacecraft design and system architecture through various diagrams. We extend the SysML diagram set to include fault propagation diagrams, which map the environment, failure manifestations, anomalies, failure effects and responses (mitigation measures) of components and systems. The SEAM platform provides a low-cost alternative to conventional radiation hardening assurance paradigms

Goal Structured Notation in a Radiation Hardening Safety Case for COTS-Based Spacecraft

A systematic approach is presented to constructing a radiation assurance case using Goal Structured Notation (GSN) for spacecraft containing COTS parts. The GSN paradigm is applied to an SRAM single-event upset experiment board designed to fly on a CubeSat November 2016. Construction of a radiation assurance case without use of hardened parts or extensive radiation testing is discussed

A Framework for Reliability and Safety Analysis of Complex Space Missions

Long duration and complex mission scenarios are characteristics of NASA's human exploration of Mars, and will provide unprecedented challenges. Systems reliability and safety will become increasingly demanding and management of uncertainty will be increasingly important. NASA's current pioneering strategy recognizes and relies upon assurance of crew and asset safety. In this regard, flexibility to develop and innovate in the emergence of new design environments and methodologies, encompassing modeling of complex systems, is essential to meet the challenges

SYSTEMS ENGINEERING AND ASSURANCE MODELING (SEAM): A WEB-BASED SOLUTION FOR INTEGRATED MISSION ASSURANCE

We present an overview of the Systems Engineering and Assurance Modeling (SEAM) platform, a web-browser-based tool which is designed to help engineers evaluate the radiation vulnerabilities and develop an assurance approach for electronic parts in space systems. The SEAM framework consists of three interconnected modeling tools, a SysML compatible system description tool, a Goal Structuring Notation (GSN) visual argument tool, and Bayesian Net and Fault Tree extraction and export tools. The SysML and GSN sections also have a coverage check application that ensures that every radiation fault identified on the SysML side is also addressed in the assurance case in GSN. The SEAM platform works on space systems of any degree of radiation hardness but is especially helpful for assessing radiation performance in systems with commercial-off-the-shelf (COTS) electronic components

Connecting Mission Profiles and Radiation Vulnerability Assessment

Radiation vulnerability assessment early in spacecraft development is cheaper and faster than in late development phases. RGENTIC and SEAM are two software platforms that can be coupled to provide this type of early assessment. Specifically, RGENTIC is a tool that outputs descriptions of radiation risks based on a selected mission environment and the system’s electronic part portfolio, while SEAM models how radiation-induced faults in electronic parts propagate through a system. In this work, we propose a spacecraft evaluation flow where RGENTIC’s outputs, which are radiation vulnerabilities of electronic parts for a given mission, become inputs to SEAM, resulting in an automatic part-type template palette presented to users so that they can easily begin modeling the occurrence and propagation of radiation-induced faults in their spacecraft. In this context, fault propagation modeling shows how radiation effects impact the spacecraft’s electronics. The interface between these platforms can be streamlined through the creation of a SEAM global part-type library with templates based on radiation effects in part-type families such as sensors, processors, voltage regulators, and so forth. Several of the part-types defined in RGENTIC have been integrated into SEAM templates. Ultimately, all 66+ part-types from the RGENTIC look-up table will be included in the SEAM global part library. Once accomplished, the part templates can be used to populate each project-specific part library in SEAM, ensuring all RGENTIC’s part-types are represented, and the radiation effects are consistent between the two. The harmonization process between RGENTIC and SEAM begins as follows: designers input a detailed knowledge of their system and mission into RGENTIC, which then outputs a generic part-type list that associates each part-type with potential radiation concerns. The list is then downloaded in a SEAM-readable file, which SEAM uses to populate the initially blank project with the part templates that correspond to RGENTIC’s output. The final product is a system fault model using a project-specific radiation effect part library. The radiation effects considered in the part library are associated with three categories of radiation-environment issues: single event effects (SEE), total ionizing dose (TID), and displacement damage dose (DDD). An example part-type is the discrete LED, which has been functionally decomposed into input power and output light. It has a single possible radiation-induced fault that is associated with DDD, which causes degraded brightness and is observed on the output. Overall, designers will benefit from a coordination of these two tools because it simplifies the initial definition of the project in SEAM. This is especially the case for new users, since the necessary radiation models for their parts are available before modeling commences. Furthermore, starting from a duplicate of an existing project decreases the amount of time and effort required to develop project-specific models. Incorporating RGENTIC’s table of part-types resolves these issues and provides a streamlined process for creating system radiation fault models. Consequently, spacecraft designers can identify radiation problems early in the design cycle and fix them with lower cost and less effort than in later design stages

Online Small-Sat Knowledge Repositories and Modeling Tools for Risk Reduction and Enhanced Mission Success

Developers of small satellites face the challenges of a short development schedule, incomplete information about parts from vendors, and limited budgets. The situation is complicated by the need to use commercial electronic parts rather than radiation hardened parts to meet performance and budget constraints. Fortunately, a number of organizations are standing up new modeling and data resources that are available for free online. Motivation for sharing models, part data, and lessons learned include shorter development times, increased mission success likelihood, and training the next generation of space professionals. The purpose of this paper is to make these resources and their complementary capabilities known to the small satellite community. Five free online platforms are discussed that address various parts of the information and modeling challenges posed by when electronics are operated in space, especially commercial electronics. The first platform is the Small Satellite Reliability Initiative (SSRI) Knowledge Base, a Wikipedia-like site that documents knowledge useful for successful small satellite missions. The second platform is Radiation Guidelines for Notional Threat Identification and Classification (RGENTIC), which accepts user input on electronic part types and mission parameters, then produces a list of possible radiation concerns for various part-types. The third platform is Systems Engineering Assurance and Modeling (SEAM), which incorporates architectural system modeling for identifying radiation fault propagation, Goal Structuring Notation, a visual argument scheme for creating radiation assurance cases, and construction of fault trees and Bayesian nets for reliability analysis. The fourth platform is the Parts, Materials and Processes Encyclopedia (PMPedia), a repository for information on relevant information for small satellite performance on electronic parts, material properties, and constructing processes. The fifth platform is Cosmic Ray Effects on Microelectronics (CREME), a collection of tools that enable the user to estimate the effect of the cosmic ray environment on various microelectronic parts. Taken together, these tools can significantly improve the chance of mission success

Methodology for Correlating Historical Degradation Data to Radiation-Induced Degradation System Effects in Small Satellites

When constructing a system-level fault tree to demonstrate device-to-system level radiation degradation, reliability engineers need relevant, device-level failure probabilities to incorporate into reliability models. Deriving probabilities from testing can be expensive and time-consuming, especially if the system is complex. This methodology offers an alternative means of deriving device-level failure probabilities. It uses Bayesian analysis to establish links between historical radiation datasets and failure probabilities. A demonstration system for this methodology is provided, which is a TID response of a linear voltage regulator at 100 krad(SiO2). Data fed into the Bayesian model is derived from literature on the components found within a linear voltage regulator. An example is presented with data pertaining to the device’s bipolar junction transistor (BJT)’s gain degradation factor (GDF). Kernel density estimation is used to provide insight into the dataset’s general distribution shape. This guides the engineer into picking the appropriate distribution for device-level Bayesian analysis. Failure probabilities generated from the Bayesian analysis are incorporated into a LTspice model to derive a system failure probability (using Monte Carlo) of the regulator’s output. In our demonstration system, a 96.5% likelihood of system degradation was found in the assumed environment