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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Progress on Machine Learning for the SNS High Voltage Converter Modulators

The High-Voltage Converter Modulators (HVCM) used to power the klystrons in the Spallation Neutron Source (SNS) linac were selected as one area to explore machine learning due to reliability issues in the past and the availability of large sets of archived waveforms. Progress in the past two years has resulted in generating a significant amount of simulated and measured data for training neural network models such as recurrent neural networks, convolutional neural networks, and variational autoencoders. Applications in anomaly detection, fault classification, and prognostics of capacitor degradation were pursued in collaboration with the Jefferson Laboratory, and early promising results were achieved. This paper will discuss the progress to date and present results from these efforts.

Pappas, Chris↗
DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Real-time Data Analytics for Condition Monitoring of Complex Industrial Systems

Modern industrial systems are now fitted with several sensors for condition monitoring. This is advantageous because these sensors can provide mass amounts of data that have the potential for aiding in tasks such as fault detection, diagnosis, and prognostics. However, the information valuable for performing these tasks is often clouded in noise and must be mined from high-dimensional data structures. Therefore, this dissertation presents a data analytics framework for performing these condition monitoring tasks using high-dimensional data. Demonstrations of this framework are detailed for challenges related to power generation systems in automobiles, power plants, and aircraft engines. These implementations leverage data collected from state-of-the-art, industry class test-rigs. Results indicate the ability of this framework to develop effective methodologies for condition monitoring of complex systems.

Peters, Benjamin↗
DOE OSTI2021

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and Maintenance (O&M) activities are some of the largest costs in operating legacy light-water plants. By reducing O&M costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. Improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages. To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and data science based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the fiscal year 2020 research progress encompassing (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Prognostic and health management of active assets in nuclear power plants

This study presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and two wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2014

A Testbed for Implementing Prognostic Methodologies on Cryogenic Propellant Loading Systems

Prognostics technologies determine the health state of a system and predict its remaining useful life. With this information, operators are able to make maintenance-related decisions, thus effectively streamlining operational and mission-level activities. Experimentation on testbeds representative of critical systems is very useful for the maturation of prognostics technology; precise emulation of actual fault conditions on such a testbed further validates these technologies. In this paper we present the development of a pneumatic valve testbed, initial experimental results and progress towards the maturation and validation of component-level prognostic methods in the context of cryogenic refueling operations. The pneumatic valve testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. The pneumatic valve testbed also contains a battery used to power some pneumatic components, enabling the study of the effects of battery degradation on the operation of the valves.

Prognositcs↗
NASA NTRS1992

Research and technology goals and objectives for Integrated Vehicle Health Management (IVHM)

Integrated Vehicle Health Management (IVHM) is defined herein as the capability to efficiently perform checkout, testing, and monitoring of space transportation vehicles, subsystems, and components before, during, and after operational This includes the ability to perform timely status determination, diagnostics, and prognostics. IVHM must support fault-tolerant response including system/subsystem reconfiguration to prevent catastrophic failures; and IVHM must support the planning and scheduling of post-operational maintenance. The purpose of this document is to establish the rationale for IVHM and IVHM research and technology planning, and to develop technical goals and objectives. This document is prepared to provide a broad overview of IVHM for technology and advanced development activities and, more specifically, to provide a planning reference from an avionics viewpoint under the OAST Transportation Technology Program Strategic Plan.

Source record↗
NASA NTRS2021

Aircraft Engine Run-to-Failure Dataset Under Real Flight Conditions for Prognostics and Diagnostics

A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health respectively fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.

CMAPPS↗
DOE OSTI2016

Online Monitoring of Induction Motors

The online monitoring of active components project, under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program, researched diagnostic and prognostic models for alternating current induction motors (IM). Idaho National Laboratory (INL) worked with the Electric Power Research Institute (EPRI) to augment and revise the fault signatures previously implemented in the Asset Fault Signature Database of EPRI’s Fleet Wide Prognostic and Health Management (FW PHM) Suite software. Induction Motor diagnostic models were researched using the experimental data collected by Idaho State University. Prognostic models were explored in the set of literature and through a limited experiment with 40HP to seek the Remaining Useful Life Database of the FW PHM Suite.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2004

Autonomous Propulsion System Technology Being Developed to Optimize Engine Performance Throughout the Lifecycle

The goal of the Autonomous Propulsion System Technology (APST) project is to reduce pilot workload under both normal and anomalous conditions. Ongoing work under APST develops and leverages technologies that provide autonomous engine monitoring, diagnosing, and controller adaptation functions, resulting in an integrated suite of algorithms that maintain the propulsion system's performance and safety throughout its life. Engine-to-engine performance variation occurs among new engines because of manufacturing tolerances and assembly practices. As an engine wears, the performance changes as operability limits are reached. In addition to these normal phenomena, other unanticipated events such as sensor failures, bird ingestion, or component faults may occur, affecting pilot workload as well as compromising safety. APST will adapt the controller as necessary to achieve optimal performance for a normal aging engine, and the safety net of APST algorithms will examine and interpret data from a variety of onboard sources to detect, isolate, and if possible, accommodate faults. Situations that cannot be accommodated within the faulted engine itself will be referred to a higher level vehicle management system. This system will have the authority to redistribute the faulted engine's functionality among other engines, or to replan the mission based on this new engine health information. Work is currently underway in the areas of adaptive control to compensate for engine degradation due to aging, data fusion for diagnostics and prognostics of specific sensor and component faults, and foreign object ingestion detection. In addition, a framework is being defined for integrating all the components of APST into a unified system. A multivariable, adaptive, multimode control algorithm has been developed that accommodates degradation-induced thrust disturbances during throttle transients. The baseline controller of the engine model currently being investigated has multiple control modes that are selected according to some performance or operational criteria. As the engine degrades, parameters shift from their nominal values. Thus, when a new control mode is swapped in, a variable that is being brought under control might have an excessive initial error. The new adaptive algorithm adjusts the controller gains on the basis of the level of degradation to minimize the disruptive influence of the large error on other variables and to recover the desired thrust response.

Litt, Jonathan S.↗
NASA NTRS2013

Prognostics

Knowledge discovery, statistical learning, and more specifically an understanding of the system evolution in time when it undergoes undesirable fault conditions, are critical for an adequate implementation of successful prognostic systems. Prognosis may be understood as the generation of long-term predictions describing the evolution in time of a particular signal of interest or fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem. Predictions are made using a thorough understanding of the underlying processes and factor in the anticipated future usage.

Systems Health Management↗
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 NTRS2011

Model-Based Diagnostics for Propellant Loading Systems

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

Daigle, Matthew John↗
DOE OSTI2019

A Prognostics and Health Management Framework for Wind

Operation and maintenance costs are a major driver for levelized cost of energy of wind power plants and can be reduced through optimized operation and maintenance practices accomplishable by various prognostics and health management (PHM) technologies. In recent years, the wind industry has become more open to adopting PHM solutions, especially those focusing on diagnostics. However, prognostics activities are, in general, still at the research and development stage. On the other hand, the industry has a request to estimate a component's remaining useful life (RUL) when it has faulted, and this is a key output of prognostics. Systematically presenting PHM technologies to the wind industry by highlighting the RUL prediction need potentially helps speed up its acceptance and provides more benefits from PHM to the industry. In this paper, we introduce a PHM for wind framework. It highlights specifics unique to wind turbines and features integration of data and physics domain information and models. The output of the framework focuses on RUL prediction. To demonstrate its application, a data domain method for wind turbine gearbox fault diagnostics is presented. It uses supervisory control and data acquisition system time series data, normalizes gearbox temperature measurements with reference to environmental temperature and turbine power, and leverages big data analytics and machine-learning techniques to make the model scalable and the diagnostics process automatic. Another physics-domain modeling method for RUL prediction of wind turbine gearbox high-speed-stage bearings failed by axial cracks is also discussed. Bearing axial cracking has been shown to be the prevalent wind turbine gearbox failure mode experienced in the field and is different from rolling contact fatigue, which is targeted during the bearing design stage. The method uses probability of failure as a component reliability assessment and RUL prediction metric, which can be expanded to other drivetrain components or failure modes. The presented PHM for wind framework is generic and applicable to both land-based and offshore wind turbines.

17 WIND ENERGY↗
NASA NTRS2014

Embedded Data Acquisition Tools for Rotorcraft Diagnostic Sensors

Rotorcraft drive trains must withstand enormous pressure while operating continuously in extreme temperature and vibration environments. Captive components, such as planetary and spiral bevel gears, see enormous strain but are not accessible to fixed instrumentation, such as a piezoelectric transducer. Thus, it is difficult to directly monitor components that are most susceptible to damage. This innovation is a self-contained data processing unit within a specialized fixture that installs directly inside the rotating pinion gear in the gearbox. From this location, it detects and transmits high-resolution prognostic data to a fixed transceiver. The sensor is based on microelectromechanical systems (MEMS) technology and uses innovative circuit designs to capture high-bandwidth data and transmit it wirelessly from inside an operational helicopter transmission. With Ridgetop's advanced MEMS-based sensor, researchers have, for the first time, been able to extract high-resolution acoustic signatures wirelessly from sensors within the transmission that would otherwise be muffled by background gear noises. Ridgetop's innovative instrument will help researchers perform dynamic analysis of gear interaction and develop improved designs for gear components. In addition, data from this instrument can be used to validate new algorithms that detect and predict faults based on external acoustic signatures, for prognostic purposes. The result of this work will be an improvement in safety, performance, and cost for future generations of rotating components.

Wagoner, Robert↗
DOE OSTI2014

Diagnostic and Prognostic Models for Generator Step-Up Transformers

In 2014, the online monitoring (OLM) of active components project under the Light Water Reactor Sustainability program at Idaho National Laboratory (INL) focused on diagnostic and prognostic capabilities for generator step-up transformers. INL worked with subject matter experts from the Electric Power Research Institute (EPRI) to augment and revise the GSU fault signatures previously implemented in the Electric Power Research Institute’s (EPRI’s) Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. Two prognostic models were identified and implemented for GSUs in the FW-PHM Suite software. INL and EPRI demonstrated the use of prognostic capabilities for GSUs. The complete set of fault signatures developed for GSUs in the Asset Fault Signature Database of the FW-PHM Suite for GSUs is presented in this report. Two prognostic models are described for paper insulation: the Chendong model for degree of polymerization, and an IEEE model that uses a loading profile to calculates life consumption based on hot spot winding temperatures. Both models are life consumption models, which are examples of type II prognostic models. Use of the models in the FW-PHM Suite was successfully demonstrated at the 2014 August Utility Working Group Meeting, Idaho Falls, Idaho, to representatives from different utilities, EPRI, and the Halden Research Project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1994

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

This paper presents a summary of the transmission diagnostics research work conducted at NASA Lewis Research Center over the last four years. In 1990, the Transmission Health and Usage Monitoring Research Team at NASA Lewis conducted a survey to determine the critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods, improved fault detection in planetary systems, and damage magnitude assessment and prognostics research were all critical to a highly reliable health and usage monitoring system. In response to this, a variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue data include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Overall results indicate that, of the gear fault detection techniques, no one method can successfully detect all possible failure modes. The more successful methods need to be integrated into a single more reliable detection technique. A recently developed method, NA4, in addition to being one of the more successful gear fault detection methods, was also found to exhibit damage magnitude estimation capabilities.

Zakajsek, James J.↗