William A. Maul

Addressing the Real-World Challenges in the Development of Propulsion IVHM Technology Experiment (PITEX)

‡‡ The Propulsion IVHM Technology Experiment (PITEX) has been an on-going research effort conducted over several years. PITEX has developed and applied a model-based diagnostic system for the main propulsion system of the X -34 reusable launch vehicle, a space-launch technology demonstrator. The application was simulation-based using detailed models of the propulsion subsystem to generate nominal and failure scenarios during captive carry, which is the most safety-critical portion of the X-34 flight. Since no system-level testing of the X-34 Main Propulsion System (MPS) was performed, these simulated data were used to verify and validate the software system. Advanced diagnostic and signal processing algorithms were developed and tested in real -time on flight-like hardware. In an attempt to expose potential performance problems, these PITEX algorithms were subject to numerous real-world effects in the simulated data includ ing noise, sensor resolution, command/valve talkback information, and nominal build variations. The current research has demonstrated the potential benefits of model-based diagnostics, defined the performance metrics required to evaluate the diagnostic system, and s tudied the impact of real-world challenges encountered when monitoring propulsion subsystems.

Application of Diagnostic Analysis Tools to the Ares I Thrust Vector Control System

The NASA Ares I Crew Launch Vehicle is being designed to send astronauts into Earth orbit in support of missions to the International Space Station, to the moon, and beyond. The launch vehicle is an in-line two-stage rocket with the crew vehicle, Orion, on top of the stack. The Ares I is undergoing design and development utilizing commercial-off-the-shelf tools and hardware when applicable, along with cutting edge launch technologies and state-of-theart design and development techniques to ensure a safe, reliable, cost-effective space transportation system. In support of the vehicle’s design and development, the Ares Functional Fault Analysis group was tasked to develop an Ares Vehicle Diagnostic Model (AVDM) and to demonstrate the capability of that model to support failure-related analyses and design integration. The AVDM is a directed graph model of failure effect propagation paths within the vehicle architecture and is a comprehensive representation of the system’s failure space behavior. The AVDM is intended to support system engineering activities during the design process, and to provide diagnostic support throughout the development and deployment of the Ares I Launch Vehicle. During the Ares I design phase, the AVDM has been demonstrated to be valuable in the systems engineering process for assessing the completeness of schematics, and improving quality of various system design documents and analyses. The AVDM, along with supporting tools, has provided detection and fault isolation information to determine which components meet the diagnostic requirements for launch pad replacement and to assess system response to off-nominal conditions. One important component of the AVDM is the Upper Stage (US) Thrust Vector Control (TVC) diagnostic model—a representation of the failure space of the US TVC subsystem. This paper first presents an overview of the AVDM, its development approach, and the software used to implement the model and conduct diagnostic analysis. It then uses the US TVC diagnostic model to illustrate details of the development, implementation, analysis, and verification processes. Finally, the paper describes how the AVDM model can impact both design and

Qualitative model-based diagnostics for rocket systems

A diagnostic software package is currently being developed at NASA LeRC that utilizes qualitative model-based reasoning techniques. These techniques can provide diagnostic information about the operational condition of the modeled rocket engine system or subsystem. The diagnostic package combines a qualitative model solver with a constraint suspension algorithm. The constraint suspension algorithm directs the solvers operation to provide valuable fault isolation information about the modeled system. A qualitative model of the Space Shuttle Main Engines oxidizer supply components was generated. A diagnostic application based on this qualitative model was constructed to process four test cases: three numerical simulations and one actual test firing. The diagnostic tools fault isolation output compared favorably with the input fault condition.

Extended Testability Analysis Tool User Guide

This paper provides an overview of the analysis and reporting capabilities of the Extended Testability Analysis (ETA) Tool which is currently being prepared for release by researchers at the NASA Glenn Research Center. The ETA Tool is a software tool that augments the analysis and reporting capabilities of a commercial-off-the-shelf (COTS) testability analysis software package. An initial diagnostic assessment is performed by the COTS software using a qualitative, directed-graph model of the system being analyzed. The testability analysis from the COTS software provides failure effect detection and fault isolation metrics, and generates a dependency matrix that correlates the system failure modes with the tests available to detect those failure modes. The ETA Tool accesses system design information captured within the COTS-based diagnostic model, along with testability analysis output from the COTS software, and creates a series of six reports for various system engineering needs. The ETA Tool also allows the user to perform additional studies on the testability analysis results, by determining the detection sensitivity to the loss of certain sensors or tests. The ETA Tool was developed to support the NASA Ares I Crew Launch Vehicle design and development. The Ares Functional Fault Analysis (FFA) group was tasked to develop a diagnostic model that would become part of the Ares ground-based diagnostic system. The FFA group selected the COTS software to build a collection of subsystem models. These models were developed independently, while adhering to a set of FFA project-defined modeling conventions. The subsystem models were subsequently integrated into a vehicle-level diagnostic model. The diagnostic models have proven to be valuable system engineering tools, providing consistency in the verification of system engineering requirements and of results from various design studies. In addition to being requested by the Upper Stage Thrust Vector Control Design Team for off-nominal analysis and analytical verification of recoverable fault requirements, analysis reports from the ETA Tool have been requested by several Ares System Engineering groups, including: Ascent Risk Analysis, Launch Commit Criteria and Ground Logistics and Supportability.

Propulsion Health Management System Development for Affordable and Reliable Operation of Space Exploration Systems

The constraints of future Exploration Missions will require unique integrated system health management capabilities throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for repair or replacement, and long communication delays, all require an integrated approach to health management that can span distinct, yet interdependent vehicle subsystems, anticipate failure states, provide autonomous remediation and support the Exploration Mission from beginning to end. Propulsion is a critical part of any space exploration mission, and monitoring the health of the propulsion system is an integral part of assuring mission safety and success. Health management is a somewhat ubiquitous technology that encompasses a large spectrum of physical components and logical processes. For this reason, it is essential to develop a systematic plan for propulsion health management system development. This paper provides a high-level perspective of propulsion health management systems, and describes a logical approach for the future planning and early development that are crucial to planned space exploration programs. It also presents an overall approach, or roadmap, for propulsion health management system development and a discussion of the associated roadblocks and challenges.

Functional Fault Model Development Process to Support Design Analysis and Operational Assessment

A functional fault model (FFM) is an abstract representation of the failure space of a given system. As such, it simulates the propagation of failure effects along paths between the origin of the system failure modes and points within the system capable of observing the failure effects. As a result, FFMs may be used to diagnose the presence of failures in the modeled system. FFMs necessarily contain a significant amount of information about the design, operations, and failure modes and effects. One of the important benefits of FFMs is that they may be qualitative, rather than quantitative and, as a result, may be implemented early in the design process when there is more potential to positively impact the system design. FFMs may therefore be developed and matured throughout the monitored systems design process and may subsequently be used to provide real-time diagnostic assessments that support system operations. This paper provides an overview of a generalized NASA process that is being used to develop and apply FFMs. FFM technology has been evolving for more than 25 years. The FFM development process presented in this paper was refined during NASAs Ares I, Space Launch System, and Ground Systems Development and Operations programs (i.e., from about 2007 to the present). Process refinement took place as new modeling, analysis, and verification tools were created to enhance FFM capabilities. In this paper, standard elements of a model development process (i.e., knowledge acquisition, conceptual design, implementation & verification, and application) are described within the context of FFMs. Further, newer tools and analytical capabilities that may benefit the broader systems engineering process are identified and briefly described. The discussion is intended as a high-level guide for future FFM modelers.