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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Simulation-Based Verification of Livingstone Applications

AI software is viewed as a means to give greater autonomy to automated systems, capable of coping with harsh and unpredictable environments in deep space missions. Autonomous systems pose a serious challenge to traditional test-based verification approaches, because of the enormous space of possible situations that they aim to address. Before these systems are put in control of critical applications, appropriate new verification approaches need to be developed. This article describes Livingstone PathFinder (LPF), a verification tool for autonomous diagnosis applications based on NASA's Livingstone model-based diagnosis system. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the Livingstone diagnosis system embedded in a simulated operating environment. The article describes different facets of LPF and reports some experimental results from applying LPF to a Livingstone model of the main propulsion feed subsystem of the X-34 space vehicle. This paper describes Livingstone PathFinder (LPF), a verification tool for autonomous diagnosis applications based on NASA's Livingstone model-based diagnosis system. LPF applies state space exploration algorithms to an instrumented testbed, consisting of the Livingstone diagnosis system embedded in a simulated operating environment. Section 2 provides an overview of Livingstone; Section 3 describes the LPF architecture; Section 4 discusses its applicability; Section 5 reviews some experimental results; Section 6 compares LPF to related verification approaches; Section 7 draws conclusions and discusses some perspectives.

Lindsey, Anthony E.↗
NASA NTRS2008

Efficient Probabilistic Diagnostics for Electrical Power Systems

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Mengshoel, Ole J.↗
NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗
NASA NTRS2025

Assessing the Added Value of Miniature X-Ray in the Setting of Portable Ultrasound in Spaceflight

INTRODUCTION: Point-of-care ultrasound (POCUS) has become the standard of care for imaging diagnosis and management in low-Earth Orbit (LEO) spaceflight and it has long been hypothesized that POCUS will also be the standard of care for exploration spaceflight. However, like the trajectory for which ultrasound became more portable and user-friendly, the mass, volume, and power requirements of radiography devices for both diagnostic and therapeutic applications have also been dramatically reduced. This study seeks to determine the clinical utility and added value of miniature x-ray (XR) for the diagnosis and management of each of the 119 conditions within NASA Exploration Medical Capability’s IMPACT Condition List (ICL) given that the medical system is presumed to already be carrying a handheld portable POCUS device. METHODS: For each condition, a team of reviewers performed a rapid systematic literature review seeking sensitivity and specificity data for both handheld portable ultrasound and miniature XR. When there was a paucity of data, subject matter expertise and clinical experience was added to semi-quantitatively score the added value of miniature XR, given an US was already available for both diagnosis and management. Diagnostic utility of a modality for a condition was evaluated in the setting of both the best- and worst-case scenario definitions included and defined by the ICL. Evidence tracing and quality of evidence scores were also recorded. RESULTS/DISCUSSION: Conditions for which it was determined that miniature XR added diagnostic or therapeutic value are provided in this presentation. Previously presented work by our team has demonstrated that XR provides diagnostic and management capabilities that are hypothesized to complement or surpass ultrasound for over one-third of medical conditions that may arise during exploration spaceflight (i.e., diagnosis of injuries to the axial skeleton, teeth, and lungs as well as management of orthopedic reductions, endotracheal tube placement, and drain placement confirmation). In the setting of known inclusion of a handheld portable POCUS device, there remains significant added value of portable miniature XR. Whether or not this added clinical benefit is worth the mass, volume, and power requirements of the radiography system remains yet unknown and is the focus of future work. LEARNING OBJECTIVES: 1) Understand the value of ultrasound and radiography in the diagnosis and management of medical comorbidities that may arise in exploration spaceflight; 2) Understand the medical conditions of highest concern on exploration class missions for which miniature x-ray may provide added value to portable ultrasound.

J G Steller↗
NASA NTRSDate not supplied

Assessing the Added Value of Miniature X-Ray in the Setting of US in Spaceflight

INTRODUCTION: Point-of-care ultrasound (POCUS) has become the standard of care for imaging diagnosis and management in lowEarth Orbit (LEO) spaceflight and it has long been hypothesized that POCUS will also be the standard of care for exploration spaceflight. However, like the trajectory for which ultrasound became more portable and user-friendly, the mass, volume, and power requirements of radiography devices for both diagnostic and therapeutic applications have also been dramatically reduced. This study seeks to determine the clinical utility and added value of miniature x-ray (XR) for the diagnosis and management of each of the 119 conditions within NASA Exploration Medical Capability’s IMPACT Condition List (ICL) given that the medical system is presumed to already be carrying a handheld portable POCUS device. METHODS: For each condition, a team of reviewers performed a rapid systematic literature review seeking sensitivity and specificity data for both handheld portable ultrasound and miniature XR. When there was a paucity of data, subject matter expertise and clinical experience was added to semi-quantitatively score the added value of miniature XR, given an US was already available for both diagnosis and management. Diagnostic utility of a modality for a condition was evaluated in the setting of both the best- and worst-case scenario definitions included and defined by the ICL. Evidence tracing and quality of evidence scores were also recorded. RESULTS/DISCUSSION: Conditions for which it was determined that miniature XR added diagnostic or therapeutic value are provided in this presentation. Previously presented work by our team has demonstrated that XR provides diagnostic and management capabilities that are hypothesized to complement or surpass ultrasound for over one-third of medical conditions that may arise during exploration spaceflight (i.e., diagnosis of injuries to the axial skeleton, teeth, and lungs as well as management of orthopedic reductions, endotracheal tube placement, and drain placement confirmation). In the setting of known inclusion of a handheld portable POCUS device, there remains significant added value of portable miniature XR. Whether or not this added clinical benefit is worth the mass, volume, and power requirements of the radiography system remains yet unknown and is the focus of future work. LEARNING OBJECTIVES: 1) Understand the value of ultrasound and radiography in the diagnosis and management of medical comorbidities that may arise in exploration spaceflight; 2) Understand the medical conditions of highest concern on exploration class missions for which miniature x-ray may provide added value to portable ultrasound.

Jon Steller↗
NASA NTRS2016

Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype.The diagnosis is performed by using a model-based method in the context of health management of hybrid systems.In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

health monitoring↗
NASA NTRS1990

Dynamic test input generation for multiple-fault isolation

Recent work is Causal Reasoning has provided practical techniques for multiple fault diagnosis. These techniques provide a hypothesis/measurement diagnosis cycle. Using probabilistic methods, they choose the best measurements to make, then update fault hypotheses in response. For many applications such as computers and spacecraft, few measurement points may be accessible, or values may change quickly as the system under diagnosis operates. In these cases, a hypothesis/measurement cycle is insufficient. A technique is presented for a hypothesis/test-input/measurement diagnosis cycle. In contrast to generating tests a priori for determining device functionality, it dynamically generates tests in response to current knowledge about fault probabilities. It is shown how the mathematics previously used for measurement specification can be applied to the test input generation process. An example from an efficient implementation called Multi-Purpose Causal (MPC) is presented.

Schaefer, Phil↗
NASA NTRS1990

Real-time diagnostics of the reusable rocket engine using on-line system identification

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Guo, T.-H.↗
NASA NTRS1990

Diagnosing multiple faults in SSM/PMAD

Multiple fault diagnosis for SSM/PMAD (space station module/power management and distribution) using the knowledge management design system as applied to the SSM/PMAD domain (KNOMAD-SSM/PMAD) is discussed. KNOMAD-SSM/PMAD provides a powerful facility for knowledge representation and reasoning which has been used to build the second generation of FRAMES (fault recovery and management expert system). FRAMES now handles the diagnosis of multiple faults and provides support for a more powerful interface for user interaction during autonomous operation. There are two types of multiple fault diagnosis handled in FRAMES. The first diagnoses hard faults, soft faults, and incipient faults simultaneously. The second diagnoses multiple hard faults which occur in close proximity in time to one another. Multiple fault diagnosis in FRAMES is performed using a rule-based approach. This rule-based approach, enabled by the KNOMAD-SSM/PMAD system, has proven to be powerful. Levels of autonomy are discussed, focusing on the approach taken in FRAMES for providing at least three levels of autonomy: complete autonomy, partial autonomy, and complete manual mode.

Riedesel, Joel↗
NASA NTRS2001

Particle Filters for Real-Time Fault Detection in Planetary Rovers

Planetary rovers provide a considerable challenge for robotic systems in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have on-board fault detection and diagnosis capabilities in order to determine the actual state of the vehicle, and decide what actions are safe to perform. Traditional model-based diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle's performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weakeners are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems.

Dearden, Richard↗
NASA NTRS2003

Issues and Concerns in Robotic Drilling

Exploration of the Martian subsurface will be essential in the search for life and water, given the desiccated and highly oxidized conditions on the surface. Discovery of these, at least in non-fossil form, is unlikely without drilling or other physical access to the subsurface. Hence subsurface access will be critical for both future in-situ science and Mars sample return. Drilling applications present many new challenges for diagnosis and control technology. Traditionally, diagnosis has concentrated on determining the internal state of a system, and detecting failures of system components. In the case of drilling applications, an additional challenge is to diagnose the interactions between the drill and its environment. This is necessary because particular observations of the drilling operation may be consistent with a number of possible problems, including faults in the equipment, but also changes in the material being drilled (for example, from rock to ice). The diagnosis of a particular observation may also depend on knowledge of geological formations previously encountered during drilling, and different remedial actions may be required for each diagnosis. Current 2009 Mars mission scenarios call for no more than 33 sols to be spent drilling. Yet they also call for a baseline of two 2m-deep holes in each of three target areas, for a total of six drilling operations. Using current levels of automation, it is estimated that 15-16 sols would be required to drill each hole. As a result of this, either the drilling part of the mission plan will need to be severely downscoped to no more than two holes total, or on-board automation and robotics must be increased in order to reduce the number of sols required per hole by removing ground control from the drilling control loop. This lecture will discuss salient issues and concerns of robotic drilling automation compares with other applications, and implementation constraints.

Glass, Brian↗
NASA NTRS2005

Two Methods for Efficient Solution of the Hitting-Set Problem

A paper addresses much of the same subject matter as that of Fast Algorithms for Model-Based Diagnosis (NPO-30582), which appears elsewhere in this issue of NASA Tech Briefs. However, in the paper, the emphasis is more on the hitting-set problem (also known as the transversal problem), which is well known among experts in combinatorics. The authors primary interest in the hitting-set problem lies in its connection to the diagnosis problem: it is a theorem of model-based diagnosis that in the set-theory representation of the components of a system, the minimal diagnoses of a system are the minimal hitting sets of the system. In the paper, the hitting-set problem (and, hence, the diagnosis problem) is translated from a combinatorial to a computational problem by mapping it onto the Boolean satisfiability and integer- programming problems. The paper goes on to describe developments nearly identical to those summarized in the cited companion NASA Tech Briefs article, including the utilization of Boolean-satisfiability and integer- programming techniques to reduce the computation time and/or memory needed to solve the hitting-set problem.

Vatan, Farrokh↗