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At least 127 records · Page 7

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NASA NTRS2003

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

Dempsey, Paula J.↗

NASA NTRS2004

Inductive System Health Monitoring

The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.

Iverson, David L.↗

NASA NTRS2005

INDUCTIVE SYSTEM HEALTH MONITORING WITH STATISTICAL METRICS

Model-based reasoning is a powerful method for performing system monitoring and diagnosis. Building models for model-based reasoning is often a difficult and time consuming process. The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS processes nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. In particular, a clustering algorithm forms groups of nominal values for sets of related parameters. This establishes constraints on those parameter values that should hold during nominal operation. During monitoring, IMS provides a statistically weighted measure of the deviation of current system behavior from the established normal baseline. If the deviation increases beyond the expected level, an anomaly is suspected, prompting further investigation by an operator or automated system. IMS has shown potential to be an effective, low cost technique to produce system monitoring capability for a variety of applications. We describe the training and system health monitoring techniques of IMS. We also present the application of IMS to a data set from the Space Shuttle Columbia STS-107 flight. IMS was able to detect an anomaly in the launch telemetry shortly after a foam impact damaged Columbia's thermal protection system.

Iverson, David L.↗

DOE OSTI2025

Virtual Self-Excited Induction Generator-Based Grid-Forming Inverter Control for Robust Voltage Regulation Under Nonideal Loading

This paper presents a generator-inspired control methodology for grid-forming (GFM) inverters that deliberately emulates a self-excited induction generator so that the inverter can hold its voltage and frequency under difficult loading and severe terminal disturbances across wide voltage and frequency ranges. The design integrates a Lyapunov energy function-based inner loop to provide high bandwidth and strong disturbance rejection, and it complements this with a passivity-based argument that furnishes a coherent large-signal stability guarantee beyond small-signal limits. Analytical insights are developed via the Krylov-Bogoliubov-Mitropolsky averaging method, which reveals an intrinsic resistive droop characteristic; these closed-form relations both explain the observed dynamics and yield simple, decentralized tuning rules. The methodology is validated on a controller-hardware-in-the-loop platform and exercised in real time across balanced, unbalanced, and nonlinear loads, as well as during parallel operation. Across these scenarios, the inverter maintains balanced three-phase voltages, limits harmonic content, settles quickly with well-damped transients, and remains resilient when multiple units operate in parallel. The contributions are a self-excited-machine-inspired GFM controller with enhanced dynamic performance and robustness, a single stability rationale grounded in passivity, closed-form expressions that guide tuning, and comprehensive hardware-in-the-loop validations demonstrating effectiveness and superiority under challenging operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Graph theory inspired anomaly detection at the LHC

Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collider events. In this work, we develop a graph autoencoder as an unsupervised, model-agnostic tool for anomaly detection, using the LHC Olympics dataset as a benchmark. By representing jet constituents as a graph, we introduce a method to systematically control the information available to the model through sparse graph constructions that serve as physically motivated inductive biases. Specifically, (1) we construct graph autoencoders based on locally rigid Laman graphs and globally rigid unique graphs, and (2) we explore the clustering of jet constituents into subjets to interpolate between high- and low-level input representations. We obtain the best performance, measured in terms of the Significance Improvement Characteristic curve for an intermediate level of subjet clustering and certain sparse unique graph constructions. We further investigate the role of graph connectivity in jet classification tasks. Our results demonstrate the potential of leveraging graph-theoretic insights to refine and increase the interpretability of machine learning tools for collider experiments.

Automation↗

NASA NTRS1978

Shock tube and shock wave research; Proceedings of the Eleventh International Symposium, University of Washington, Seattle, Wash., July 11-14, 1977

Papers are presented on the applications of shock-wave technology to the study of hydrodynamics, the use of the pressure-wave machine for charging diesel engines, and measurements of the heat-transfer rate in gas-turbine components. Consideration is given to shock propagation along 90-degree bends, the explosive dissemination of liquids, and rotational and vibrational relaxation behind weak shock waves in water vapor. Shock phenomena associated with expansion flows are described and stratospheric-related research using the shock tube is outlined. Attention is given to shock-wave ignition of magnesium powders, Mach reflection and boundary layers, and transition in the shock-induced unsteady boundary layer on a flat plate. Shock-tube measurements of induction and post-induction rates for low-Btu gas mixtures are presented and shock-initiated ignition in COS-N2O-Ar mixtures is described. Cluster growth rates in supersaturated lead vapor are presented and a study of laser-induced plasma motion in a solenoidal magnetic field is reviewed.

Ahlborn, B.↗

DOE OSTI2025

Evaluation of Damage in Medium Voltage Cable Using Machine Learning

Developments in cable test instrumentation coupled with artificial intelligence and machine learning (ML) to aid in interpretation of cable test signals supports the feasibility for automated analysis of reflectometry tests for low voltage power cables. This work seeks to leverage prior ML work and success for low voltage cables to evaluate potential application to medium voltage (2kV to 10kV) installations. The Accelerated and Real-Time Environmental Nodal Assessment (ARENA) Cable Motor Test Bed at Pacific Northwest National Laboratory (PNNL) was used to test a medium voltage cable with several types of damage including thermal aging and low resistance conductor-to-shield faults. The cable was tested using an inductive clamshell coupler to protect the test instruments from the energized cable voltages that would damage the test instruments if coupled directly to the energized conductor.

42 ENGINEERING↗

DOE OSTI2026

Postirradiation Examination of WIRE-21 Experiment Irradiated in the High Flux Isotope Reactor

Westinghouse Electric Company is developing wireless sensors to monitor the centerline temperature and internal pressure of commercial light-water reactor fuel rods during irradiation. Oak Ridge National Laboratory and Westinghouse Electric Company developed the Wireless Instrumented RB Experiment 2021 (WIRE-21) to test wireless temperature and pressure sensor technologies in a removable beryllium position in the High Flux Isotope Reactor. The experiment was irradiated for a total of 75 days, at temperatures ranging from approximately 150°C to 400°C, resulting in a peak fast (energy > 0.1 MeV) neutron fluence of about 3 × 10 21 n/cm 2 . The temperature was intentionally cycled multiple times to compare the response of the wireless temperature sensor to collocated thermocouples. Similarly, the pressure sensor was actuated in multiple steps to compare the response of the wireless measurement to excore pressure transducers (Petrie et al., 2023). After irradiation, the experiment was disassembled in the Irradiated Fuels Examination Laboratory (IFEL) hot cell at Oak Ridge National Laboratory with the intent to recover sensor and dosimetry components, document the as-irradiated condition of the hardware, and investigate possible causes of the sensor behavior observed during irradiation. The postirradiation examination successfully recovered and preserved key WIRE-21 components. After the housing was removed using a milling machine, the internal experiment sections were examined. All eight fiber-optic sensors were recovered, cut, and stored. The silicon carbide thermometry, temperature sensor, pressure sensor, lower spacer, and selected pressure and temperature cable sections were also removed and stored. The metal bellows of the pressure sensor was found to be plastically deformed, indicating that it had been properly pressurized during irradiation and generally behaved as expected. X-ray diffraction analysis of a section of one of the irradiated inductor cores within the pressure sensor was performed and confirmed the presence of phase-pure alpha ferrite (i.e., no unexpected phase transformations). Inductance testing was performed on the irradiated pressure sensor cores using an unirradiated test coil, and DC resistance measurements of the transceiver coils were also performed. The measurements with irradiated inductor cores assembled inside unirradiated coils showed slightly reduced inductance compared to measurements made with unirradiated cores, but the difference was not sufficient to explain the more significant reductions in inductance that were observed in-pile. Therefore, it is suspected that degradation of the inductor coils (specifically the wire wrapping) is responsible for the reduced inductance observed in-pile.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1991

Passive acquisition of CLIPS rules

The automated acquisition of knowledge by machine has not lived up to expectations, and knowledge engineering remains a human intensive task. Part of the reason for the lack of success is the difference in the cognitive focus of the expert. The expert must shift his or her focus from the subject domain to that of the representation environment. In doing so this cognitive shift introduces opportunity for errors and omissions. Presented here is work that observes the expert interact with a simulation of the domain. The system logs changes in the simulation objects and the expert's actions in response to those changes. This is followed by the application of inductive reasoning to move the domain specific rules observed to general domain rules.

Kovarik, Vincent J., Jr.↗

NASA NTRS2006

Unitary Shaft-Angle and Shaft-Speed Sensor Assemblies

The figure depicts a unit that contains a rotary-position or a rotary-speed sensor, plus electronic circuitry necessary for its operation, all enclosed in a single housing with a shaft for coupling to an external rotary machine. This rotation sensor unit is complete: when its shaft is mechanically connected to that of the rotary machine and it is supplied with electric power, it generates an output signal directly indicative of the rotary position or speed, without need for additional processing by other circuitry. The incorporation of all of the necessary excitatory and readout circuitry into the housing (in contradistinction to using externally located excitatory and/or readout circuitry) in a compact arrangement is the major difference between this unit and prior rotation-sensor units. The sensor assembly inside the housing includes excitatory and readout integrated circuits mounted on a circular printed-circuit board. In a typical case in which the angle or speed transducer(s) utilize electromagnetic induction, the assembly also includes another circular printed-circuit board on which the transducer windings are mounted. A sheet of high-magnetic permeability metal ("mu metal") is placed between the winding board and the electronic-circuit board to prevent spurious coupling of excitatory signals from the transducer windings to the readout circuits. The housing and most of the other mechanical hardware can be common to a variety of different sensor designs. Hence, the unit can be configured to generate any of variety of outputs by changing the interior sensor assembly. For example, the sensor assembly could contain an analog tachometer circuit that generates an output proportional (in both magnitude and sign or in magnitude only) to the speed of rotation.

Alhorn, Dean C.↗

DOE OSTI2025

An Additively Manufactured Fe-3Si Stator for a High-Performance Electrical Motor

Additive manufacturing (AM) has the potential to produce novel high-performance electrical machines, enabling the direct printing of complex shapes and the simultaneous processing of multiple feedstocks in a single build. We examined the properties and functional performance of Fe-3 wt.% Si materials that were printed via selective laser melting, machined down to thin laminates, and stacked to form a stator core of a prototype brushless permanent-magnet electrical motor. Big Area Additive Manufacturing of Nd2Fe14B (NdFeB)–polyphenylene sulfide (PPS) bonded magnets was performed, with them then being magnetized and used for the rotor. The magnetic, mechanical, and electrical properties of the as-printed and various heat-treated thin laminates and the back electromotive force (EMF) of the electrical motors at different rotational speeds were measured. The thin laminates exhibit a maximum relative permeability of 7494 at an applied field of 0.8 Oe and a core loss of about 20 W/lb at 60 Hz with the maximum induction of 15 kg. In addition to the demonstration of AM printing, motor assembly, and complete characterization of printed Fe-3 wt.% Si, this report highlights the areas of improvement needed in printing technologies to achieve AM built electrical motors and the need for isotropic microstructure refinements to make the laminates appropriate for high-mechanical-strength and low-loss rotational electrical devices.

36 MATERIALS SCIENCE↗

NASA NTRS2002

Software Development for the Hobby-Eberly Telescope's Segment Alignment Maintenance System using LABView

The software development for an upgrade to the Hobby-Eberly Telescope (HET) was done in LABView. In order to improve the performance of the HET at the McDonald Observatory, a closed-loop system had to be implemented to keep the mirror segments aligned during periods of observation. The control system, called the Segment Alignment Maintenance System (SAMs), utilized inductive sensors to measure the relative motions of the mirror segments. Software was developed in LABView to tie the sensors, operator interface, and mirror-control motors together. Developing the software in LABView allowed the system to be flexible, understandable, and able to be modified by the end users. Since LABView is built using block diagrams, the software naturally followed the designed control system's block and flow diagrams, and individual software blocks could be easily verified. LABView's many built-in display routines allowed easy visualization of diagnostic and health-monitoring data during testing. Also, since LABView is a multi-platform software package, different programmers could develop the code remotely on various types of machines. LABView s ease of use facilitated rapid prototyping and field testing. There were some unanticipated difficulties in the software development, but the use of LABView as the software "language" for the development of SAMs contributed to the overall success of the project.

Hall, Drew P.↗

NASA NTRS1993

Using machine learning techniques to automate sky survey catalog generation

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Fayyad, Usama M.↗

DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗

NASA NTRSDate not supplied

Combined Analysis of NASA's High Efficiency Megawatt Motor and Its Converter

NASA’s High Efficiency Megawatt Motor (HEMM) is being developed to achieve the performance needed by single aisle class electrified aircraft. It is a 1.4 MW electric machine designed as a generator for NASA’s STARC-ABL concept vehicle. It has performance objectives of greater than 16 kW/kg electromagnetic specific power and efficiency of greater than 98%. The key enabling technology for HEMM’s high performance metrics is a superconducting rotor cooled by an integrated cryocooler. HEMM’s integrated cryocooler is being designed to only lift 50 W of heat at its target 50 K cold tip operating temperature. HEMM’s superconducting rotor therefore needs to have essentially zero electromagnetic losses in order to stay below the 50 W heat rejection limit of the cryocooler. This paper presents the combined analysis of HEMM and a preliminarily designed HEMM converter to analyze the rotor loss impact of several potential converter filter topologies. The analysis results point to interleaving and series inductance filters not being sufficient to suppress switching harmonic caused rotor loss. Paths forward to develop a converter to eliminate rotor loss are discussed.

Matthew Granger↗

NASA NTRSDate not supplied

Combined Analysis of NASA's High Efficiency Megawatt Motor and Its Converter

Matthew Granger↗

DOE OSTI2026

Experimental study of ECH pre-ionization on J-TEXT

An experimental study on electron cyclotron heating (ECH) pre-ionization has been conducted on J-TEXT in support of the joint experiment research for ITER plasma initiation. In this experiment, ECH power was injected to the vessel before the application of loop voltage ionize the neutral gas and form the initial plasma or so-called pre-plasma. The impact of several significant factors, such as magnetic field configuration, pre-fill gas pressure, ECH toroidal injection angle and ECH power on the evolution of pre-plasma are systematically studied, aiming to identify shared features, clarify their potential relationship and optimize the discharge parameters to generate a rather high pre-plasma density. By separating ECH power from the inductive start-up, the effect of pre-plasma on tokamak start-up can be observed. A dynamic magnetic configuration facilitates the transition of pre-plasma to tokamak plasma. To assess the influence of pre-plasma density on tokamak start-up, two kinds of magnetic field configurations are examined. While the effect of different pre-plasma densities on tokamak start-up is negligible, a significant difference is observed between pure ohmic start-up and start-up with pre-ionization. The studies presented here show evolutionary trends and threshold values needed to optimize ECH pre-ionization and also a feasible way to improve pre-plasma density and a configuration to stabilize pre-plasma for transition. Eventually, these results may contribute to multi-machine research and physics analysis that can assist ITER with its optimal preparation for first plasma operation.

ECH↗

NASA NTRS1993

Automatic array alignment in data-parallel programs

FORTRAN 90 and other data-parallel languages express parallelism in the form of operations on data aggregates such as arrays. Misalignment of the operands of an array operation can reduce program performance on a distributed-memory parallel machine by requiring nonlocal data accesses. Determining array alignments that reduce communication is therefore a key issue in compiling such languages. We present a framework for the automatic determination of array alignments in array-based, data-parallel languages. Our language model handles array sectioning, reductions, spreads, transpositions, and masked operations. We decompose alignment functions into three constituents: axis, stride, and offset. For each of these subproblems, we show how to solve the alignment problem for a basic block of code, possibly containing common subexpressions. Alignments are generated for all array objects in the code, both named program variables and intermediate results. We assign computation to processors by virtue of explicit alignment of all temporaries; the resulting work assignment is in general better than that provided by the 'owner-computes' rule. Finally, we present some ideas for dealing with control flow, replication, and dynamic alignments that depend on loop induction variables.

Chatterjee, Siddhartha↗