SEARCH · Engineering Papers

Results for “DIAGNOSIS”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Leveraging Structures in Fault Diagnosis for Lithium-Ion Battery Packs

Lithium-ion battery systems consist of a varying number of single cells, designed to meet specific application requirements for output voltage and capacity. Effective fault diagnosis in these battery systems is an essential prerequisite for ensuring their safe and reliable operation. To address this need, we introduce a novel model-based fault diagnosis approach that distinguishes itself by leveraging informative structures inherent in battery systems such as architecture, uniformity among the constituent cells, and sparsity of fault occurrences to enhance its fault diagnosis capabilities. The proposed approach formulates a moving horizon estimation (MHE) problem, incorporating such structural information to estimate different fault signals—specifically, internal short circuits, external short circuits, and voltage and current sensors faults. We conduct various simulations to evaluate the performance of the proposed approach under different fault types and magnitudes. The obtained results validate the proposed approach and promise effective fault diagnosis for battery systems.

Farakhor, Amir↗

DOE OSTI2024

Online Dynamic Cyber-Attack Diagnosis in Power Electronics Systems Based on Few-Shot Learning

With increasing exposure to software-based sensing and control, power electronics systems are facing higher risks of cyber-physical attacks. To ensure system stability and minimize potential economic losses, it is critical to monitor the operating states and detect those attacks at the early stage. However, anomaly detection and diagnosis of attacks are still challenging, especially when labeled anomaly data is difficult or even infeasible to obtain. To overcome this problem, we propose a Few-Shot Learning (FSL) based approach for cyber-attack diagnosis leveraging the waveform data. To the best of our knowledge, this work is the first attempt at leveraging FSL for cyber-attack diagnosis in power electronics systems. Extensive experimental results demonstrate that our proposed approach can achieve comparable diagnosis accuracy with the state-of-the-art data-driven methods using less than 0.04% of the training samples.

Li, Qi↗

DOE OSTI2023

Longitudinal Changes in Immune Activation Serum Biomarkers Prior to Diagnosis and Risk of B-cell NHL Subtypes

To examine the contribution of B-cell activation molecules to B-cell follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL), a prospective study was conducted using pre-diagnosis serial serum samples from the US Department of Defense Serum Repository. Each case (n = 142 FL, n = 211 DLBCL) was matched to two controls on age, gender, race, military branch, and blood collection dates. Immune activation molecules (IL1β, IL2, IL4, IL5, IL6, IL10, IL12, CXCL13, IL8, TNFα, IFNγ, GM-CSF, VEGF, sCD30, IgE) were quantified using ELISA or multiplex immunometric (Luminex) assay. Longitudinal data were analyzed using linear mixed modeling. As serial specimens were collected over several years before diagnosis, we evaluated the temporal dynamics of these markers. Increased serum levels of sCD30, CXCL13, and to a lesser extent IL10, were associated with both FL and DLBCL in cases compared with controls, with a median follow-up of 5.5 years from the earliest specimen collection to diagnosis date. Significant increasing sCD30 and CXCL13 trajectories for FL and DLBCL subtypes were noted starting at the earliest time points and with IL10 levels increasing significantly at time points closer to diagnosis. In conclusion, these results suggest that sCD30, CXCL13, and IL10 may contribute to the etiology of FL and DLBCL and are potential biomarkers for these non–Hodgkin lymphoma subtypes. The increasing trajectories of the B-cell activation molecules, sCD30, CXCL13, and to a lesser extent IL10, may indicate early disease-induced effects or reflect the chronic stimulation of B-cells that promotes the development of FL and DLBCL subtypes.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1988

Knowledge-based diagnosis for aerospace systems

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

Atkinson, David J.↗

NASA NTRS1988

Knowledge-based diagnosis for aerospace systems

Atkinson, David↗

NASA NTRS1991

Robust fault diagnosis of physical systems in operation

Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.

Abbott, Kathy Hamilton↗

NASA NTRS1990

Object-oriented fault tree models applied to system diagnosis

When a diagnosis system is used in a dynamic environment, such as the distributed computer system planned for use on Space Station Freedom, it must execute quickly and its knowledge base must be easily updated. Representing system knowledge as object-oriented augmented fault trees provides both features. The diagnosis system described here is based on the failure cause identification process of the diagnostic system described by Narayanan and Viswanadham. Their system has been enhanced in this implementation by replacing the knowledge base of if-then rules with an object-oriented fault tree representation. This allows the system to perform its task much faster and facilitates dynamic updating of the knowledge base in a changing diagnosis environment. Accessing the information contained in the objects is more efficient than performing a lookup operation on an indexed rule base. Additionally, the object-oriented fault trees can be easily updated to represent current system status. This paper describes the fault tree representation, the diagnosis algorithm extensions, and an example application of this system. Comparisons are made between the object-oriented fault tree knowledge structure solution and one implementation of a rule-based solution. Plans for future work on this system are also discussed.

Iverson, David L.↗

NASA NTRS1992

Intelligent fault isolation and diagnosis for communication satellite systems

Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past diagnosis studies.

Tallo, Donald P.↗

NASA NTRS1992

Fault diagnosis for the Space Shuttle main engine

A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Fault diagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.

Duyar, Ahmet↗

NASA NTRS1992

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Duyar, A.↗

NASA NTRS1991

Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Duyar, A.↗

NASA NTRS1994

Matrix Failure Modes and Effects Analysis as a Knowledge Base for a Real Time Automated Diagnosis Expert System

Failure Modes and Effects Analysis contain a wealth of information that can be used to create the knowledge base required for building automated diagnostic Expert systems. A real time monitoring and diagnosis expert system based on an actual NASA project's matrix failure modes and effects analysis was developed. This Expert system Was developed at NASA Ames Research Center. This system was first used as a case study to monitor the Research Animal Holding Facility (RAHF), a Space Shuttle payload that is used to house and monitor animals in orbit so the effects of space flight and microgravity can be studied. The techniques developed for the RAHF monitoring and diagnosis Expert system are general enough to be used for monitoring and diagnosis of a variety of other systems that undergo a Matrix FMEA. This automated diagnosis system was successfully used on-line and validated on the Space Shuttle flight STS-58, mission SLS-2 in October 1993.

Herrin, Stephanie↗

NASA NTRS2004

A New On-Line Diagnosis Protocol for the SPIDER Family of Byzantine Fault Tolerant Architectures

This paper presents the formal verification of a new protocol for online distributed diagnosis for the SPIDER family of architectures. An instance of the Scalable Processor-Independent Design for Electromagnetic Resilience (SPIDER) architecture consists of a collection of processing elements communicating over a Reliable Optical Bus (ROBUS). The ROBUS is a specialized fault-tolerant device that guarantees Interactive Consistency, Distributed Diagnosis (Group Membership), and Synchronization in the presence of a bounded number of physical faults. Formal verification of the original SPIDER diagnosis protocol provided a detailed understanding that led to the discovery of a significantly more efficient protocol. The original protocol was adapted from the formally verified protocol used in the MAFT architecture. It required O(N) message exchanges per defendant to correctly diagnose failures in a system with N nodes. The new protocol achieves the same diagnostic fidelity, but only requires O(1) exchanges per defendant. This paper presents this new diagnosis protocol and a formal proof of its correctness using PVS.

Geser, Alfons↗

NASA NTRS2004

Online model-based diagnosis to support autonomous operation of an advanced life support system

This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

Non-NASA Center↗

NASA NTRS2008

Damage Diagnosis in Semiconductive Materials Using Electrical Impedance Measurements

Recent aerospace industry trends have resulted in an increased demand for real-time, effective techniques for in-flight structural health monitoring. A promising technique for damage diagnosis uses electrical impedance measurements of semiconductive materials. By applying a small electrical current into a material specimen and measuring the corresponding voltages at various locations on the specimen, changes in the electrical characteristics due to the presence of damage can be assessed. An artificial neural network uses these changes in electrical properties to provide an inverse solution that estimates the location and magnitude of the damage. The advantage of the electrical impedance method over other damage diagnosis techniques is that it uses the material as the sensor. Simple voltage measurements can be used instead of discrete sensors, resulting in a reduction in weight and system complexity. This research effort extends previous work by employing finite element method models to improve accuracy of complex models with anisotropic conductivities and by enhancing the computational efficiency of the inverse techniques. The paper demonstrates a proof of concept of a damage diagnosis approach using electrical impedance methods and a neural network as an effective tool for in-flight diagnosis of structural damage to aircraft components.

Ross, Richard W.↗

NASA NTRS2017

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

Warner, James E.↗

NASA NTRSDate not supplied

Harnessing Artificial Intelligence for Medical Diagnosis and Treatment During Space Exploration Missions

BACKGROUND The medical capabilities necessary for long-duration exploration missions (LDEMs) will differ tremendously from those currently available to crew medical officers (CMOs) on the International Space Station (ISS). Ground support will be more challenging due to distance-related communication delays and data transmission, and resource utilization must be optimized given limited ability for resupply. Clinical decision support systems (CDSSs) can help mitigate these limitations. The recent launch of generative artificial intelligence (AI) tools based upon large language models (LLM) support the creation of a smart assistant for onboard triage, diagnosis, and guided treatment of medical conditions during these missions. The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool can help predict which clinical problems and outcomes are likely to occur for a design reference mission (DRM) and assist Medical Operations and systems engineering teams in creating a medical system that may optimally mitigate the predicted risks. The purpose of this study was to identify AI tools currently available or in development for the assistive diagnosis and care of medical conditions predicted for an extended duration Lunar mission. METHODS The 119 medical conditions currently built into the IMPACT suite were categorized into systems, and these diagnoses were used as keywords for our literature search. Using PubMed and Google Scholar, we performed a literature survey of AI tools applicable to these conditions. Article inclusion criteria included publication between the years 2017-2023, as the sentinel paper discussing the “selective attention” driving ChatGPT and other generative transformer models was published in June 2017. Where applicable, we reviewed only the top 1000 research articles (based on relevance) for each of the keywords/phrases. AI tools whose training sets were exclusive to a pediatric patient population were excluded. We also excluded any medical diagnostic tools (such as CT, MRI, mass spectrometry) or procedures (such as endoscopy, surgery) that are unlikely to be available during LDEMs due to mass and volume constraints, CMO knowledge, skills, and abilities, and/or inherent procedural risks. RESULTS Our survey highlighted several AI-driven tools for the triage, diagnosis, and management of those medical conditions highlighted by IMPACT. Selected publications for each medical condition were then screened for inclusion within ten systems-based categories including: general diagnostic tools (25), tools to diagnose and manage respiratory (40), dermatologic (34), neurologic (28), auditory and vestibular (30), ophthalmic (34), musculoskeletal (104), infection-associated (92), and gynecologic (19) conditions, as well as tools that could be deployed in the setting of trauma and emergency (34). CONCLUSIONS Numerous AI-driven tools were highlighted within this literature survey, ranging from chatbot assistants that triage knee pain to vision transformer models for diagnosis of ophthalmic conditions using ocular surface images captured with a mobile phone. Remaining challenges include optimizing connectivity and integration of existing and developing systems into the vehicles or habitats. Notably, findings from this survey could help guide the initial design of an all-encompassing, onboard medical AI assistant for use during future LDEMs.

R A Lacinski↗

NASA NTRS2003

Distributed real-time model-based diagnosis

This paper presents an approach to onboard anomaly diagnosis that combines the simplicity and real-time guarantee of a rule-based diagnosis system with the specification ease and coverage guarantees of a model-based diagnosis system.

autonomy diagnosis↗