SEARCH · Search NASA

Results for “Prognostics and Health Management”

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 37 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2014

Distributed Prognostics based on Structural Model Decomposition

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

centrifugal pump↗

NASA NTRS2011

Towards Prognostics of Electrolytic Capacitors

A remaining useful life prediction algorithm and degradation model for electrolytic capacitors is presented. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management research. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. In particular, experimental results of an accelerated aging test under electrical stresses are presented. The capacitors used in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors.

Celaya, Jose R.↗

NASA NTRS2012

Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress

Electrolytic capacitors are used in several applications ranging from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining useful life of components or systems based on their current state of health and their anticipated future use and operational conditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress conditions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.

Kulkarni, Chetan S.↗

NASA NTRS2012

A Briefing on Metrics and Risks for Autonomous Decision-Making in Aerospace Applications

Significant technology advances will enable future aerospace systems to safely and reliably make decisions autonomously, or without human interaction. The decision-making may result in actions that enable an aircraft or spacecraft in an off-nominal state or with slightly degraded components to achieve mission performance and safety goals while reducing or avoiding damage to the aircraft or spacecraft. Some key technology enablers for autonomous decision-making include: a continuous state awareness through the maturation of the prognostics health management field, novel sensor development, and the considerable gains made in computation power and data processing bandwidth versus system size. Sophisticated algorithms and physics based models coupled with these technological advances allow reliable assessment of a system, subsystem, or components. Decisions that balance mission objectives and constraints with remaining useful life predictions can be made autonomously to maintain safety requirements, optimal performance, and ensure mission objectives. This autonomous approach to decision-making will come with new risks and benefits, some of which will be examined in this paper. To start, an account of previous work to categorize or quantify autonomy in aerospace systems will be presented. In addition, a survey of perceived risks in autonomous decision-making in the context of piloted aircraft and remotely piloted or completely autonomous unmanned autonomous systems (UAS) will be presented based on interviews that were conducted with individuals from industry, academia, and government.

Frost, Susan↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗

NASA NTRSDate not supplied

SmallSat / Model-Based Mission Assurance Session Overview

SmallSat state of the art report advanced avionics section updates. Systematic CubeSat radiation hardening assurance using the SEAM platforms. Building ontologies for AI-integrated prognostic health management of spacecraft avionics.

Rebekah Austin↗

NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗

NASA NTRS2012

Bayesian Framework Approach for Prognostic Studies in Electrolytic Capacitor under Thermal Overstress Conditions

Electrolytic capacitors are used in several applications ranging from power supplies for safety critical avionics equipment to power drivers for electro-mechanical actuator. Past experiences show that capacitors tend to degrade and fail faster when subjected to high electrical or thermal stress conditions during operations. This makes them good candidates for prognostics and health management. Model-based prognostics captures system knowledge in the form of physics-based models of components in order to obtain accurate predictions of end of life based on their current state of heal th and their anticipated future use and operational conditions. The focus of this paper is on deriving first principles degradation models for thermal stress conditions and implementing Bayesian framework for making remaining useful life predictions. Data collected from simultaneous experiments are used to validate the models. Our overall goal is to derive accurate models of capacitor degradation, and use them to remaining useful life in DC-DC converters.

Kulkarni, Chetan S.↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗

NASA NTRSDate not supplied

Assessment of ProgPy - An Open-Source Condition Monitoring and Diagnostics Tool

Traditional maintenance programs, such as corrective and preventive strategies, may lead to high costs and operational inefficiencies. Condition Monitoring and Diagnostics (CM&D) aims to improve these maintenance strategies by enabling timely insights into equipment health and performance. However, implementation of CM&D can be challenging without a robust framework that manages data efficiently, supports interoperability and simplifies integration. To address these challenges ProgPy, an open-source Python-based prognostics tool developed by NASA Ames Research Center, offers a structured solution for broader Prognostics and Health Management (PHM) applications. Ongoing research is assessing the feasibility of implementing ProgPy as a Condition Monitoring and Diagnostics (CM&D) solution by comparing its framework to the guidelines for open CM&D systems recommended in the ISO 13374-2 standard. This evaluation aims to highlight ProgPy’s strengths and identify opportunities for improvement, thereby, contributing to its advancement as an effective tool for Prognostics and Health Management (PHM). This paper presents the results of an initial assessment of the ProgPy toolbox through a gearbox case study using open-source datasets.

Condition-Monitoring, Diagnostics, Failure, Gearbo↗

NASA NTRS2014

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem several benchmarking datasets have been collected by NASA's prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

Uncertainty↗

NASA NTRS2021

An Uncertainty Quantification Framework for Autonomous Flight System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.

Uncertainty Quantification↗

NASA NTRS2020

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Banerjee, Portia↗

NASA NTRS2014

A Physics-Based Modeling Framework for Prognostic Studies

Prognostics and Health Management (PHM) methodologies have emerged as one of the key enablers for achieving efficient system level maintenance as part of a busy operations schedule, and lowering overall life cycle costs. PHM is also emerging as a high-priority issue in critical applications, where the focus is on conducting fundamental research in the field of integrated systems health management. The term diagnostics relates to the ability to detect and isolate faults or failures in a system. Prognostics on the other hand is the process of predicting health condition and remaining useful life based on current state, previous conditions and future operating conditions. PHM methods combine sensing, data collection, interpretation of environmental, operational, and performance related parameters to indicate systems health under its actual application conditions. The development of prognostics methodologies for the electronics field has become more important as more electrical systems are being used to replace traditional systems in several applications in the aeronautics, maritime, and automotive fields. The development of prognostics methods for electronics presents several challenges due to the great variety of components used in a system, a continuous development of new electronics technologies, and a general lack of understanding of how electronics fail. Similarly with electric unmanned aerial vehicles, electrichybrid cars, and commercial passenger aircraft, we are witnessing a drastic increase in the usage of batteries to power vehicles. However, for battery-powered vehicles to operate at maximum efficiency and reliability, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. We develop an electrochemistry-based model of Li-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles.

Li-ion Batteries↗

NASA NTRS2014

Prognostics Uncertainty Management with Application to Government and Industry

Predictions about the future are contingent on future usage, but also on the quality of the models employed and the assessment of the current health state. These factors, amongst others, need to be considered to arrive at a prediction that is conducted through a rigorous method but where the confidence bounds are not prohibitively large.

Prognostics↗

NASA NTRS2011

Accelerated Aging with Electrical Overstress and Prognostics for Power MOSFETs

Power electronics play an increasingly important role in energy applications as part of their power converter circuits. Understanding the behavior of these devices, especially their failure modes as they age with nominal usage or sudden fault development is critical in ensuring efficiency. In this paper, a prognostics based health management of power MOSFETs undergoing accelerated aging through electrical overstress at the gate area is presented. Details of the accelerated aging methodology, modeling of the degradation process of the device and prognostics algorithm for prediction of the future state of health of the device are presented. Experiments with multiple devices demonstrate the performance of the model and the prognostics algorithm as well as the scope of application. Index Terms Power MOSFET, accelerated aging, prognostics

Saha, Sankalita↗

NASA NTRS2008

Self-Powered Wireless Sensors

NASA's integrated vehicle health management (IVHM) program offers the potential to improve aeronautical safety, reduce cost and improve performance by utilizing networks of wireless sensors. Development of sensor systems for engine hot sections will provide real-time data for prognostics and health management of turbo-engines. Sustainable power to embedded wireless sensors is a key challenge for prolong operation. Harvesting energy from the environment has emerged as a viable technique for power generation. Thermoelectric generators provide a direct conversion of heat energy to electrical energy. Micro-power sources derived from thermoelectric films are desired for applications in harsh thermal environments. Silicon based alloys are being explored for applications in high temperature environments containing oxygen. Chromium based p-type Si/Ge alloys exhibit Seebeck coefficients on the order of 160 micro V/K and low thermal conductance of 2.5 to 5 W/mK. Thermoelectric properties of bulk and thin film silicides will be discussed

Dynys, Fred↗

NASA NTRS2010

A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics

One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications.

Wheeler, Kevin R.↗