In this article, we show how to map a sampling of the hardest artificial intelligence problems in space exploration onto equivalent Ising models that then can be attacked using quantum annealing implemented in D-Wave machine. We overview the existing results as well as propose new Ising model implementations for quantum annealing. We review supervised and unsupervised learning algorithms for classification and clustering with applications to feature identification and anomaly detection. We introduce algorithms for data fusion and image matching for remote sensing applications. We overview planning problems for space exploration mission applications and algorithms for diagnostics and recovery with applications to deep space missions. We describe combinatorial optimization algorithms for task assignment in the context of autonomous unmanned exploration. Finally, we discuss the ways to circumvent the limitation of the Ising mapping using a "blackbox" approach based on ideas from probabilistic computing. In this article we describe the architecture of the D-Wave One machine and report its benchmarks. Results on random ensemble of problems in the range of up to 96 qubits show improved scaling for median core quantum annealing time compared with classical algorithms; whether this scaling persists for larger problem sizes is an open question. We also review previous results of D-Wave One benchmarking studies for solving binary classification problems with a quantum boosting algorithm which is shown to outperform AdaBoost. We review quantum algorithms for structured learning for multi-label classification and introduce a hybrid classical/quantum approach for learning the weights. Results of D-Wave One benchmarking studies for learning structured labels on four different data sets show a better performance compared with an independent Support Vector Machine approach with linear kernel.

A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration

Aircraft Loss of Control Problem Analysis and Research Toward a Holistic Solution

Liquid-propellant rocket engines health-monitoring—a survey

Validation of technologies developed for loss of control (LOC) prevention and recovery poses significant challenges. Aircraft LOC can result from a wide spectrum of hazards, often occurring in combination, which cannot be fully replicated during evaluation. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of hazardous and uncertain conditions, and the validation framework must provide some measure of assurance that the new vehicle safety technologies do no harm (i.e., that they themselves do not introduce new safety risks). This paper summarizes a proposed validation framework for safety-critical systems, provides an overview of validation methods and tools developed by NASA to date within the Vehicle Systems Safety Project, and develops a preliminary set of test scenarios for the validation of technologies for LOC prevention and recovery

/pdf/validation-of-safety-critical-systems-for-aircraft-loss-of-snixly6k2z.pdf

Validation of Safety-Critical Systems for Aircraft Loss-of-Control Prevention and Recovery

Over the past 20 years, there has been much work in the area of model-based diagnosis (MBD). By this we mean diagnosis systems arising from computer science or artificial intelligence approaches where a generic software engine is developed to address a large class of diagnosis problems. Later, models are created to apply the engine to a specific problem. These techniques are very attractive, suggesting a vision of machines that repair themselves, reduced costs for all kinds of endeavors, spacecraft that continue their missions even when failing, and so on. This promise inspired a broad range of activity, including our involvement over several years in flying the Livingstone and Livingstone 2 on-board model-based diagnosis and recovery systems as experiments on two spacecraft. While a great deal was learned through a variety of applications to simulators, testbeds and flight experiments, no project adopted the technology in operations and the expected benefits have not yet come to fruition. This led us to ask what are the costs of using MBD for the operational scenarios we encountered, what are the benefits, and how do we approach the question of whether the benefits outweigh the costs? How are missions today approaching fault diagnosis and recovery during operations? If we characterize the cost and benefits of using MBD, how would it compare with traditional ways of making a system more robust? How did expectations for MBD compare to benefits seen in the field and why? The literature does provide existing cost models for related endeavors such as integrated vehicle health management. It also provides excellent narratives of why projects chose not to use MBD after considering it. However, we believe that this paper is the first to unpack and discuss the cost, benefit and risk factors that impact the net value of model-based diagnosis and recovery. We use experience with systems such as Livingstone as an example, so our focus is on-board model-based diagnosis and recovery, but we believe many of the insights and remaining questions on the costs and benefits are applicable to other diagnosis applications. Quantitative model of when on-board model-based diagnosis would be an effective choice, it lays out the cost/benefit proposition and identifies several disconnects that we believe prevent adoption as an operational tool. While we do not suggest metrics for every cost, benefit and risk factor we identify, we do discuss where each factor arises in development or operations and how model-based diagnosis and recovery tends to leverage or exacerbate each. As such we believe the analysis is of use to those developing MBD or related techniques and those who may employ them. It also serves as one example of how honest expectations based on technical capability can come to differ from the net impact on customer problems. In this paper we present a cost/benefit analysis for MBD, using expectations and experiences with Livingstone as an example. We provide an overview of common techniques for making spacecraft robust, citing fault protection schemes from recent missions. We lay out the cost, benefit and risk advantages associated with on-board MBD, and use the examples to probe each expected advantage in turn. We conclude our analysis with a summary of our method for analyzing the costs and benefits in a particular domain, and encourage others to come forward with analyses of costs and benefits for fielded systems. Finally, we discuss related work both in terms of similar analyses and fielded systems.

https://atc1.aut.uah.es/~mdolores/Docs/2008/Kurien-RMoreno-IEEEAerospace.pdf

Costs and Benefits of Model-based Diagnosis

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 including 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 studied the impact of real-world challenges encountered when monitoring propulsion subsystems.

/pdf/addressing-the-real-world-challenges-in-the-development-of-4rdbz056um.pdf

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

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.

Extended Testability Analysis Tool User Guide

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 solver's operation to provide valuable fault isolation information about the modeled system. A qualitative model of the Space Shuttle Main Engine's 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 tool's fault isolation output compared favorably with the input fault condition.

/pdf/qualitative-model-based-diagnostics-for-rocket-systems-235uneuxsn.pdf

Qualitative model-based diagnostics for rocket systems

This paper describes the development and initial demonstration of a Portable Health Algorithms Test (PHALT) System that is being developed by researchers at the NASA Glenn Research Center (GRC). The PHALT System was conceived as a means of evolving the maturity and credibility of algorithms developed to assess the health of aerospace systems. Comprising an integrated hardware-software environment, the PHALT System allows systems health management algorithms to be developed in a graphical programming environment; to be tested and refined using system simulation or test data playback; and finally, to be evaluated in a real-time hardware-in-the-loop mode with a live test article. In this paper, PHALT System development is described through the presentation of a functional architecture, followed by the selection and integration of hardware and software. Also described is an initial real-time hardware-in-the-loop demonstration that used sensor data qualification algorithms to diagnose and isolate simulated sensor failures in a prototype Power Distribution Unit test-bed. Success of the initial demonstration is highlighted by the correct detection of all sensor failures and the absence of any real-time constraint violations.

/pdf/development-and-application-of-a-portable-health-algorithms-4ygrh05bin.pdf

Development and Application of a Portable Health Algorithms Test System

NASA has developed a model of the Ares-I launch vehicle to track the effects of failures through vehicle subsystems using the TEAMS Designer tool from Qualtech Systems. The model contains over 10K modules. It is believed to be more than two times larger than any other model developed using TEAMS Designer. The size of the model created several challenges for model development and integration: how to make systematic large scale changes without introducing human touch errors; how to determine what changed in the analytic results; how to verify model components against source documents; and how to present the model to system experts for review. Tools were developed at NASA to support each of these areas.

Christopher E. Fulton

Papers

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

Extended Testability Analysis Tool User Guide

Qualitative model-based diagnostics for rocket systems

Development and Application of a Portable Health Algorithms Test System

Tools Supporting Development and Integration of TEAMS Diagnostic Models