Results for “prognostics” — Page 9

DOE OSTI2021

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2012

A Comparison of Filter-based Approaches for Model-based Prognostics

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

prognostics↗

NASA NTRS2014

Current Pressure Transducer Application of Model-based Prognostics Using Steady State Conditions

Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.

Fault Estimation↗

NASA NTRS2015

Advanced Ground Systems Maintenance Prognostics Project

The project implements prognostics capabilities to predict when a component system or subsystem will no longer meet desired functional or performance criteria, called the end of life. The capability also provides an assessment of the remaining useful life of a hardware component. The project enables the delivery of system health advisories to ground system operators. This project will use modeling techniques and algorithms to assess components' health andpredict remaining life for such components. The prognostics capability being developed will beused:during the design phase and during pre/post operations to conduct planning and analysis ofsystem design, maintenance & logistics plans, and system/mission operations plansduring real-time operations to monitor changes to components' health and assess their impacton operations.This capability will be interfaced to Ground Operations' command and control system as a part ofthe AGSM project to help assure system availability and mission success. The initial modelingeffort for this capability will be developed for Liquid Oxygen ground loading applications.

Prognostics↗

NASA NTRS2013

Prognostics Applied to Electric Propulsion UAV

Health management plays an important role in operations of UAV. If there is equipment malfunction on critical components, safe operation of the UAV might possibly be compromised. A technology with particular promise in this arena is equipment prognostics. This technology provides a state assessment of the health of components of interest and, if a degraded state has been found, it estimates how long it will take before the equipment will reach a failure threshold, conditional on assumptions about future operating conditions and future environmental conditions. This chapter explores the technical underpinnings of how to perform prognostics and shows an implementation on the propulsion of an electric UAV. A particle filter is shown as the method of choice in performing state assessment and predicting future degradation. The method is then applied to the batteries that provide power to the propeller motors. An accurate run-time battery life prediction algorithm is of critical importance to ensure the safe operation of the vehicle if one wants to maximize in-air time. Current reliability based techniques turn out to be insufficient to manage the use of such batteries where loads vary frequently in uncertain environments.

Batteries↗

NASA NTRS2017

Modeling for Battery Prognostics

For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.

Prognostics↗

NASA NTRS2016

System-Level Prognostics for the National Airspace

In the National Airspace System (NAS), safety is assured through a set of rules, regulations, and procedures to respond to unsafe events. However, safety stands to benefit immensely from the introduction of tools and methodologies from Prognostics and Health Management (PHM). PHM will enable the NAS to stochastically predict unsafe states within the NAS, enabling a proactive preventative response strategy, as opposed to a reactive mitigative one. However, current PHM methods do not directly apply to the NAS for several reasons: they typically apply only at the component level, are implemented in a centralized manner, and are focused only on predicting remaining useful life. In this paper, we extend the model-based prognostics approach to PHM in order to provide a framework that can be applied to the NAS. We offer a system-level approach that supports a distributed implementation, and provide algorithms to predict the probability of an unsafe state, either at a specific time or within a time interval, and to predict the time of an unsafe state. Experimental results in simulation demonstrate the new approach.

prognostics↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A 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, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A 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, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A 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, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

DOE OSTI2011

Online Monitoring To Enable Improved Diagnostics, Prognostics and Maintenance

Only time will tell what the implications of the Fukushima incident will be. Discussions are on-going with regard to continued operation and life extension of the existing fleet, new build, and the wider policy issues including technologies needed to address spent fuel storage and ensure energy security, and the related desires to provide sustainable energy systems while at the same time limiting greenhouse gas emissions. The science base for advanced diagnostics and prognostics to support its use in nuclear power plants (NPPs) for active components (pumps, valves etc) has been demonstrated. A challenge is enabling adaption of these technologies for NPP deployment and the validation of the data from these technologies. Advanced diagnostics, monitoring and prognostics applied to passive structures, which in the USA context of longer term operation is up to 80 years, are being researched. Early laboratory work is demonstrating the potential for these methods, although technical challenges remain. It can be expected that there will be an increased need for and use of on-line monitoring for a wide range of both active and passive systems in all types of nuclear power plants.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Prognostics Health Management for Advanced Small Modular Reactor Passive Components

In the United States, sustainable nuclear power to promote energy security is a key national energy priority. Advanced small modular reactors (AdvSMR), which are based on modularization of advanced reactor concepts using non-light-water reactor (LWR) coolants such as liquid metal, helium, or liquid salt may provide a longer-term alternative to more conventional LWR-based concepts. The economics of AdvSMRs will be impacted by the reduced economy-of-scale savings when compared to traditional LWRs and the controllable day-to-day costs of AdvSMRs are expected to be dominated by operations and maintenance costs. Therefore, achieving the full benefits of AdvSMR deployment requires a new paradigm for plant design and management. In this context, prognostic health management of passive components in AdvSMRs can play a key role in enabling the economic deployment of AdvSMRs. In this paper, the background of AdvSMRs is discussed from which requirements for PHM systems are derived. The particle filter technique is proposed as a prognostics framework for AdvSMR passive components and the suitability of the particle filter technique is illustrated by using it to forecast thermal creep degradation using a physics-of-failure model and based on a combination of types of measurements conceived for passive AdvSMR components.

passive components↗

DOE OSTI2018

Prognostic Factors in Overall Survival of Patients with Unresectable Intrahepatic Cholangiocarcinoma Treated by Means of Yttrium-90 Radioembolization: Results in Therapy-Naïve Patients

IntroductionTo investigate prognostic factors in unresectable intrahepatic cholangiocarcinoma (ICC) therapy-naïve patients after yttrium-90 (Y-90) radioembolization (RE) therapy.Materials and MethodsBetween 2005 and 2016, 21 patients with ICC were treated with Y-90 RE only and their survival data were analyzed. Patients were stratified and response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) criteria.ResultThe overall median survival was 15 months. Survival was significantly (p = 0.009) prolonged in patients with tumor burden of ≤ 25% (n = 8, OS 37.5 months) versus those with a tumor burden of 25–50% (n = 13, OS 15 months). The other variables: tumor morphology (infiltrative vs. peripheral), tumor distribution (solitary vs. multifocal), lobes involved (unilobar vs. bilobar), FDG PET status (FDG avid vs. non-avid), RE treatment sessions (1 session vs. 2 sessions), metastases (metastasis vs. no metastasis) and RECIST criteria, had no significant impact on survival.ConclusionTumor burden represents a key prognostic factor of survival in therapy-naïve patients with unresectable ICC treated with Y-90 RE therapy only.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Prognosticating Survival in Hepatocellular Carcinoma with Elevated Baseline Alpha-fetoprotein Treated with Radioembolization Using a Novel Laboratory Scoring System: Initial Development and Validation

Aims: To investigate laboratory parameters as predictors of overall survival (OS) for hepatocellular carcinoma (HCC) treated with radioembolization and develop/validate a scoring system. Methods: With IRB approval, we included all patients with baseline alpha-fetoprotein (AFP) > 100 ng/dL from our prospectively acquired HCC radioembolization database. Neutrophil–lymphocyte ratio, albumin–bilirubin (ALBI), and AFP were measured at baseline and at 1-, 3-, and 6-month post-radioembolization Landmarks. OS was assessed from these Landmarks. Univariate/multivariate analyses were performed to evaluate OS predictability of these parameters. Baseline Imaging, Laboratory, and Combination scoring systems were developed. Developing/validating groups were created to investigate/validate the score’s OS predictability. Time-dependent receiver operating characteristics (ROC) were evaluated. Patients were stratified into groups I, II, and III by using 25th and 75th percentile cutoffs according to change in Laboratory Score from baseline. Results: 345/401 (86%), 238/401 (59%), and 167/401 (42%) patients had laboratory parameters available at the 1-, 3-, and 6-month Landmarks, respectively. ALBI and AFP were significant OS prognosticators at all Landmarks. The Laboratory Score [ALBI + (0.3 × LnAFP)] was developed/internally validated to predict OS from these Landmarks. Areas under the curve of time-dependent ROCs of the Baseline Imaging vs. Laboratory scores in predicting patient OS post 3 and 6 months Landmarks were 0.56 versus 0.82 and 0.57 versus 0.77, respectively. OS differences in groups I, II, and III according to change in Laboratory Score from baseline were significant (p < 0.001). Conclusions: Post-radioembolization AFP and ALBI scores were significant OS prognosticators. A decrease in post-therapeutic Laboratory Score, which combines AFP and ALBI, correlates with an improved OS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Identification of candidate diagnostic and prognostic biomarkers for human prostate cancer: RPL22L1 and RPS21

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. This study was designed to investigate the potential of Ribosomal Protein L22-like1 (RPL22L1) and Ribosomal Protein S21 (RPS21) as diagnostic and prognostic biomarkers for PCa. First, RPL22L1 and RPS21 were screened as the key molecular of PCa by bioinformatics analysis. Subsequently, the prostate tissue samples were stained for antibodies against RPL22L1 and RPS21. The unbiased signal quantification was performed by ImageJ software, and the results showed that the expression of RPL22L1 and RPS21 exhibited significant differences between the PCa tissues and the normal prostate tissues. Receiver-operating characteristics (ROC) curves were prepared, and then the area under the curve (AUC) values of RPL22L1 and RPS21 were calculated as 0.798 and 0.768, and the likelihood ratio (LR) values of RPL22L1 and RPS21 were calculated as 2.86 and 2.53. These data implied that the over-expression of RPL22L1 and RPS21 is associated with the presence of PCa. The further analysis suggested that the expression of RPL22L1 and RPS21 were significantly higher in high Gleason grade than they were in low Gleason grade. In addition, in vitro studies were undertaken to evaluate the roles of RPL22L1 and RPS21 in PCa. The results revealed that these genes promote PCa cell proliferation, migration and invasion, and inhibit PCa cell apoptosis. Taken together, these data showed that RPL22L1 and RPS21 exhibited higher expression in human prostate cancer tissue, and involved in PCa cell proliferation and invasion. This research provided a novel insight into diagnostic and prognostic biomarkers for PCa.

62 RADIOLOGY AND NUCLEAR MEDICINE↗