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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Qualitative Event-Based Fault Isolation under Uncertain Observations

For many systems, automatic fault diagnosis is critical to ensuring safe and efficient operation. Fault isolation is performed by analyzing measured signals from the system, and reasoning over the system behavior to determine which faults have occurred, based on models of predicted faulty behavior. For dynamic systems, reasoning may be performed using qualitative analysis of the differences between measured signals and their predicted values, in which observations take the form of qualitative symbols. Such an approach is quick to isolate faults, but depends critically on correct generation of the qualitative symbols from the signals. In this paper, we develop an approach to qualitative event-based fault isolation for dynamic systems that is robust to incorrect qualitative observations. Observations are treated as uncertain, where multiple interpretations of an observation, each with its own probability, are considered. By interpreting observed symbols in a probabilistic manner, the approach degrades gracefully as the number of incorrectly-generated symbols increases. The approach is demonstrated on an electrical power system testbed, and experiments using real data obtained from the hardware demonstrate the improved fault isolation performance in the presence of incorrect symbol generation.

Daigle, Matthew↗
NASA NTRS1986

A flight expert system (FLES) for on-board fault monitoring and diagnosis

The increasing complexity of modern aircraft creates a need for a larger number of caution and warning devices. But more alerts require more memorization and higher work loads for the pilot and tend to induce a higher probability of errors. Therefore, an architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing and recovering from in-flight faults has been developed. A prototype of FLES has been implemented. A sensor simulation model was developed and employed to provide FLES with the airplane status information during the diagnostic process. The simulator is based partly on the Lockheed Advanced Concept System (ACS), a future generation airplane, and partly on the Boeing 737, an existing airplane. A distinction between two types of faults, maladjustments and malfunctions, has led us to take two approaches to fault diagnosis. These approaches are evident in two FLES subsystems: the flight phase monitor and the sensor interrupt handler. The specific problem addressed in these subsystems has been that of integrating information received from multiple sensors with domain knowledge in order to assess abnormal situations during airplane flight. This paper describes the reasons for handling malfunctions and maladjustments separately and the use of domain knowledge in the diagnosis of each.

Ali, M.↗
NASA NTRS2003

Integrated Formulation of Beacon-Based Exception Analysis for Multimissions

Further work on beacon-based exception analysis for multimissions (BEAM), a method of real-time, automated diagnosis of a complex electromechanical systems, has greatly expanded its capability and suitability of application. This expanded formulation, which fully integrates physical models and symbolic analysis, is described. The new formulation of BEAM expands upon previous advanced techniques for analysis of signal data, utilizing mathematical modeling of the system physics, and expert-system reasoning,

Mackey, Ryan↗
DOE OSTI2024

Topological Interpretability for Deep Learning

With the growing adoption of AI-based systems across everyday life, the need to understand their decision-making mechanisms is correspondingly increasing. The level at which we can trust the statistical inferences made from AI-based decision systems is an increasing concern, especially in high-risk systems such as criminal justice or medical diagnosis, where incorrect inferences may have tragic consequences. Despite their successes in providing solutions to problems involving real-world data, deep learning (DL) models cannot quantify the certainty of their predictions. These models are frequently quite confident, even when their solutions are incorrect. This work presents a method to infer prominent features in two DL classification models trained on clinical and non-clinical text by employing techniques from topological and geometric data analysis. We create a graph of a model's feature space and cluster the inputs into the graph's vertices by the similarity of features and prediction statistics. We then extract subgraphs demonstrating high-predictive accuracy for a given label. These subgraphs contain a wealth of information about features that the DL model has recognized as relevant to its decisions. We infer these features for a given label using a distance metric between probability measures, and demonstrate the stability of our method compared to the LIME and SHAP interpretability methods. This work establishes that we may gain insights into the decision mechanism of a DL model. This method allows us to ascertain if the model is making its decisions based on information germane to the problem or identifies extraneous patterns within the data.

Spannaus, Adam↗
DOE OSTI2022

Automatic information extraction from childhood cancer pathology reports

The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification. We developed 2 models, ICD-O-3 classification and ICCC recoding (Model 1) and direct ICCC classification (Model 2), and 4 scenarios subject to the training sample size. We evaluated these models with a corpus consisting of 29206 reports with age at diagnosis between 0 and 19 from 6 state cancer registries. Our findings suggest that the direct ICCC classification (Model 2) is substantially better than reusing the ICD-O-3 classification model (Model 1). Applying the uncertainty quantification mechanism to assess the confidence of the algorithm in assigning a code demonstrated that the model achieved a micro-F1 score of 0.987 while abstaining (not sufficiently confident to assign a code) on only 14.8% of ambiguous pathology reports. Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

The Buffer Diagnostic Prototype: A fault isolation application using CLIPS

This paper describes problem domain characteristics and development experiences from using CLIPS 6.0 in a proof-of-concept troubleshooting application called the Buffer Diagnostic Prototype. The problem domain is a large digital communications subsystems called the real-time network (RTN), which was designed to upgrade the launch processing system used for shuttle support at KSC. The RTN enables up to 255 computers to share 50,000 data points with millisecond response times. The RTN's extensive built-in test capability but lack of any automatic fault isolation capability presents a unique opportunity for a diagnostic expert system application. The Buffer Diagnostic Prototype addresses RTN diagnosis with a multiple strategy approach. A novel technique called 'faulty causality' employs inexact qualitative models to process test results. Experimental knowledge provides a capability to recognize symptom-fault associations. The implementation utilizes rule-based and procedural programming techniques, including a goal-directed control structure and simple text-based generic user interface that may be reusable for other rapid prototyping applications. Although limited in scope, this project demonstrates a diagnostic approach that may be adapted to troubleshoot a broad range of equipment.

Porter, Ken↗
NASA NTRS2009

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

Duda, David P.↗
DOE OSTI2021

Hydrogen Component Leak Rate Quantification for System Risk and Reliability Assessment through QRA and PHM Frameworks: Preprint

The National Renewable Energy Laboratory's (NREL) Hydrogen Safety Research and Development (HSR&D) program in collaboration with the University of Maryland's Systems Risk and Reliability Analysis Laboratory (SyRRA) are working to improve reliability and reduce risk in hydrogen systems. This approach strives to use quantitative data on component leaks and failures, together with Prognosis and Health Management (PHM), and Quantitative Risk Assessment (QRA) to identify at-risk components, reduce component failures and downtime, and predict when components require maintenance. Hydrogen component failures increase facility maintenance cost, facility downtime, and reduce public acceptance of hydrogen technologies, ultimately increasing facility size and cost because of potentially overly conservative requirements. Leaks are a predominant failure mode for hydrogen components. However, uncertainties in the amount of hydrogen emitted from leaking components and the frequency of those failure events limit the understanding of the risks that they present under real-world operational conditions. NREL has deployed a test fixture, the Leak Rate Quantification Apparatus (LRQA), to quantify the mass flow rate of leaking gases from medium and high-pressure components that have failed while in service. Quantitative hydrogen leak rate data from this system could ultimately be used to better inform risk assessment and Regulation Codes and Standards (RCS). Parallel activity explores the use of PHM and QRA techniques to assess and reduce risk, thereby improving safety and reliability of hydrogen systems. The results of QRAs could further provide a systematic and science-based foundation for the design and implementation of RCS, as in the latest versions of the NFPA 2 code for gaseous hydrogen stations. Alternatively, data-driven techniques of PHM could provide new damage diagnosis and health-state prognosis tools. This research will help end users, station owners and operators, and regulatory bodies move towards risk-informed preventative maintenance versus emergency corrective maintenance, reducing cost and improving reliability. Predictive modelling of failures could improve safety and affect RCS requirements such as setback distances at liquid refuelling sites. The combination of leak rate quantification research, PHM, and QRA can lead to better informed models enabling data-based decision to be made for hydrogen system safety improvements.

codes and standards↗
DOE OSTI2020

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
NASA NTRS1996

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

Ramirez, W. Fred↗
DOE OSTI2024

Demonstration and Evaluation of Explainable and Trustworthy Predictive Technology for Condition-based Maintenance

The domestic nuclear power plant (NPP) fleet has historically relied on labor-intensive and time-consuming predictive maintenance (PdM) programs, thus driving up operation and maintenance (O&M) costs to achieve high-capacity factors. Artificial intelligence (AI) and machine-learning (ML) can help simplify complex problems such as diagnosing equipment degradation to enable more effective decision-making efforts. The benefits of AI will be felt through more efficient plant O&M, improved work processes, and better integration of people and technology. Together, these benefits hold the promise to make nuclear power more sustainable by reducing O&M costs while improving employee engagement. While AI and ML technologies hold significant promise for the nuclear industry, there are challenges or barriers to their adoption. Explainability and trustworthiness of AI are two salient challenges that need to be addressed for wider deployment of these technologies in NPPs. This research focuses specifically on addressing the explainability and trustworthiness of AI technologies to advance the human, technical, and organization (HTO) readiness levels in adopting a risk-informed PdM strategy at commercial NPPs. In addition, this approach can be adapted to enhance the acceptability of AI in other nuclear applications with a few application-specific modifications. The technical approach ensuring wider adoption of AI technologies was developed by Idaho National Laboratory (INL)—in collaboration with Public Service Enterprise Group (PSEG), Nuclear, LLC—by utilizing the circulating water system (CWS) at two PSEG-owned plant sites for demonstration. Focused user studies were performed in collaboration with subject matter experts (SMEs) from PSEG and other nuclear domains to enhance human and organization readiness by building trust in AI-informed technologies. VIsualization for PrEdictive maintenance Recommendation (VIPER)—a Battelle Energy Alliance, LLC, copyrighted software—was developed and expanded to provide a user-centric visualization by incorporating inputs from the collaborating utility, human factors engineering guidelines, and data analysts. The VIPER software enables users, who may be unfamiliar with ML in general, to be interactively engaged by asking technical questions about PdM, work orders, diagnosis results and their confidence levels, the kind of data being used, and the types of ML algorithms employed. This interactive engagement enhances explainability and builds trust. One of the enabling accomplishments was the integration of large language models (LLMs), both text-based and vision-based, in the VIPER software.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1990

Knowledge-based and integrated monitoring and diagnosis in autonomous power systems

A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.

Momoh, J. A.↗
DOE OSTI2020

On the Formalization of Development and Assessment Process for Digital Twins in the Nearly Autonomous Management and Control System

In recent years, the autonomous control system has been encouraged in advanced reactors for restoring economic viability, simplifying the operation and maintenance, and enabling remote-site power generations [1]. Since the reactor is expected to be operated for a long period of time with a limited number of individuals onsite, it is recommended that the autonomous control system should have access to very realistic models of the state of processes in the whole lifecycle, together with these process behaviors in interaction with their environment in the real world. As a result, digital twin (DT) technology is suggested in autonomous control systems. DT is defined as a digital representation of a physical object or system, which contains a record for the histories of loads, operation and maintenance status, predictions for the near-term transient of important state variables, and decision-making process [2]. Since machine learning (ML) can recognize patterns within a complex system in real-time applications, it has been used to build DTs in the autonomous control systems for advanced reactors. Meanwhile, due to the rareness of operation data in accident scenarios, the development and assessment of DTs is expected to be mainly driven by simulations. Although the capability and feasibility of ML-based DTs are recognized in improving the safety and efficiency of reactor control, a major concern from the regulatory commission and the nuclear industry is whether the information from a DT is developed and assessed in accordance with expectation and requirements by the target decision. Such concerns not only affect the acceptance criteria for DTs, but also values that can be extracted from DTs and autonomous control system during operations. Inspired by the success of formal methods in improving the reliability and robustness of computer programming and software development, it is suggested that the development and assessment process (DAP) for both separate DTs and integral control system should be formalized in a transparent, consistent, and improvable manner. In this study, a digital-twin development and assessment process (DT-DAP) is proposed by adapting the evaluation model development and assessment process (EMDAP) [3] to requirements by the autonomous control system, ML algorithms, and DT technology. To demonstrate the framework, a baseline nearly autonomous management and control (NAMAC) system with ML-based DTs for diagnosis and prognosis is developed and assessed based on the framework. It is found that with selected testing methods and techniques, the DT-DAP can help identify errors in DTs and NAMAC which would otherwise be left unverified. Meanwhile, it is found that the DT-DAP can improve the DTs and NAMAC by continuously learning and iterating through different elements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A cosine-based correlation information entropy approach for building automatic fault detection baseline construction

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-and-error approach to determine if a given baseline is sufficient via empirical experiments. A mechanism to support decisions such as how many samples and what samples should be included in the baseline is currently lacking. In this study, a data-driven method for AFDD baseline construction based on information entropy is developed. The entropy is derived based on cosine similarity among typical building automation system measurements in conjunction with outdoor weather information. The performance of the proposed method is evaluated using real building data. Evaluation results indicate that the fault detection strategy adopting the proposed method has similar or better accuracy in detecting faults compared to the same fault detection strategy using the baseline construction method from the literature. Additionally, the use of entropy enables the proposed method to automatically construct and assess the baseline consisting of information-rich samples.

42 ENGINEERING↗
NASA NTRS1994

Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be developed easily.

Iverson, David L.↗
NASA NTRS1988

Reasoning about fault diagnosis for the space station common module thermal control system

The proposed common module thermal control system for the Space Station is designed to integrate thermal distribution and thermal control functions in order to transport heat and provide environmental temperature control through the common module. When the thermal system is operating in an off-normal state, due to component faults, an intelligent controller is called upon to diagnose the fault type, identify the fault location and determine the appropriate control action required to isolate the faulty component. A methodology is introduced for fault diagnosis based upon a combination of signal redundancy techniques and fuzzy logic. An expert system utilizes parity space representation and analytic redundancy to derive fault symptoms, the aggregate of which is assessed by a multivalued rule based system. A subscale laboratory model of the thermal control system designed is used as the testbed for the study.

Vachtsevanos, G.↗
NASA NTRS1987

An evaluation of a real-time fault diagnosis expert system for aircraft applications

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Schutte, Paul C.↗