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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.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2017

Ecophotonics: assessment of temperature gradient in aquatic organisms using up-conversion luminescent particles

In the frameworks of developing ecophotonics, we consider the possibility of applying luminescence spectroscopy for monitoring conditions of aquatic organisms, aimed at the study and prognosis of the effect of human activity and climate changes on the environment. The method of luminescence spectroscopy in combination with anti-Stokes luminophores (up-conversion particles) used as optical sensors is used for the noninvasive assessment of the temperature gradient in the internal tissues of aquatic organisms. It is shown that the temperature dependence of the intensity ratio observed in the maxima of the luminescence spectrum bands of the particles Y{sub 2}O{sub 3} : Yb, Er, administered in a biological object, is linear. This fact offers a possibility of using the up-conversion particles for assessing the metabolic activity of different tissues, including those in the framework of ecological monitoring. (paper)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

HDG-1 Experiment Irradiation Monitoring Data Qualification Final Report

SUMMARY The U.S. Department of Energy (DOE) Advanced Reactor Technologies (ART) Graphite Research and Development (GRD) Program is conducting a series of six experiments to quantify the effects of irradiation on nuclear-grade graphite. This report documents the qualification of irradiation monitoring data for the fifth experiment, High Dose Graphite-1 (HDG-1). Qualified monitoring data are required by the ART program to support the design and licensing of the first high-temperature reactor (HTR) nuclear plant. Data are classified as Qualified if they meet the usage requirements described in the experiment planning and quality assurance (QA) documents, Failed if they do not meet those requirements and provide no usable information, or Trend if they do not fully meet all requirements but still provide useful information subject to an assessment of how any deficiencies may affect a particular use of the data. HDG-1 irradiation began with Advanced Test Reactor (ATR) Cycle 168B on August 24, 2020, and concluded after Cycle 173C on January 27, 2025. The HDG-1 capsule was removed from the reactor core twice—during core internal change (CIC) Cycle 170A and powered axial locator mechanism (PALM) Cycle 172A—to prevent overheating of the graphite specimens during high-power PALM cycles. The capsule was therefore irradiated during a total of seven normal ATR cycles: 168B, 169A, 171A, 171B, 173A, 173B, and 173C. Irradiation monitoring data evaluated in this report include thermocouple (TC) temperature, gas flow rate, gas moisture, gas pressure, specimen load, and graphite stack displacement. Temperature. A total of 14,508,065 TC temperature records were captured. Of these, 13,901,785 (95.8%) are Qualified and 606,280 (4.2%) are Failed. The principal source of failed temperature data was the instrument failure of TC-9 (Zone 2) on June 24, 2024, and TC-10 (Zone 1) on July 5, 2024, near the end of Cycle 173A, which resulted in 595,554 Failed readings. An additional 379 missing values and 10,347 slightly negative values from TC-13 during ATR outages are also Failed. Neither TC-9 nor TC-10 was used as a temperature-control TC, and their failures did not compromise capsule condition monitoring. Correlation analysis of all 13 TCs found no evidence of virtual junction formation. Control chart analysis revealed clear downward drift of approximately 80°C for TC-6 (Zone 3) relative to other stable TCs, and possible downward drift of approximately 60°C for TC-13 relative to the Zone 5 control TC (TC-1), though TC-13 remained consistent with the Zone 2 control TC (TC-12). Gas flow. A total of 20,088,090 gas flow rate records were captured. Of these, 19,941,463 (99.3%) are Qualified and 146,627 (0.7%) are Failed due to missing values. All argon, helium, and total gas flow data were within expected ranges throughout the irradiation. Gas moisture. A total of 1,116,005 outlet gas moisture values were captured. Of these, 1,101,421 (98.7%) are Qualified and 14,584 (1.3%) are Failed, comprising 14,556 out-of-range values and 28 missing values. The out-of-range moisture values exceeded 22,000 ppmv for approximately 1 week at the beginning of Cycle 173A, when accumulated moisture evaporated after the capsule was retrieved from water storage during PALM Cycle 172A and reinserted into the east flux trap. Moisture levels returned to below 25 ppmv for the remaining three cycles, and the transient high-moisture event did not affect the integrity of specimen irradiation. Gas pressure. A total of 7,812,035 gas pressure values were captured. Of these, 6,642,048 (85.0%) are Qualified and 1,169,987 (15.0%) outlet pressure values are Failed, comprising 718,537 zero outlet pressure values due to sensor failure from Cycle 168B through Cycle 171B, 54,550 missing values, and 396,900 too-low outlet pressure values, ranging from 1.1 to 1.6 psia after sensor replacement during Cycle 173A. Load. A total of 6,696,030 load values were captured. Of these, 6,694,580 (99.98%) are Qualified and 1,450 (0.02%) are Failed due to missing values. Applied loads to the six specimen stacks were stable throughout the irradiation. Stack displacement. A total of 6,696,030 displacement values were captured. Of these, 5,713,297 (85.32%) are Qualified and 3,781 (0.06%) are Failed due to missing values. Stack displacement increased consistently throughout the irradiation, reaching approximately 3.08 in. for Channels 5 and 6 by the end of irradiation. 978,952 (14.62%) substantially elevated displacements observed for Channel 6 beginning in Cycle 171A and for Channel 5 beginning in Cycle 173A are assigned Trend status. Raising pressure. A total of 1,115,999 raising pressure values were captured. Of these, 1,115,430 (99.95%) are Qualified and 569 (0.05%) are Failed due to missing values. Ram pressure. A total of 6,696,030 ram pressure values were captured. Of these, 6,692,249 (99.95%) are Qualified and 3,484 (0.05%) are Failed due to missing values. Stack raising was perf

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗
NASA NTRS2008

J-2X Abort System Development

The J-2X is an expendable liquid hydrogen (LH2)/liquid oxygen (LOX) gas generator cycle rocket engine that is currently being designed as the primary upper stage propulsion element for the new NASA Ares vehicle family. The J-2X engine will contain abort logic that functions as an integral component of the Ares vehicle abort system. This system is responsible for detecting and responding to conditions indicative of impending Loss of Mission (LOM), Loss of Vehicle (LOV), and/or catastrophic Loss of Crew (LOC) failure events. As an earth orbit ascent phase engine, the J-2X is a high power density propulsion element with non-negligible risk of fast propagation rate failures that can quickly lead to LOM, LOV, and/or LOC events. Aggressive reliability requirements for manned Ares missions and the risk of fast propagating J-2X failures dictate the need for on-engine abort condition monitoring and autonomous response capability as well as traditional abort agents such as the vehicle computer, flight crew, and ground control not located on the engine. This paper describes the baseline J-2X abort subsystem concept of operations, as well as the development process for this subsystem. A strategy that leverages heritage system experience and responds to an evolving engine design as well as J-2X specific test data to support abort system development is described. The utilization of performance and failure simulation models to support abort system sensor selection, failure detectability and discrimination studies, decision threshold definition, and abort system performance verification and validation is outlined. The basis for abort false positive and false negative performance constraints is described. Development challenges associated with information shortfalls in the design cycle, abort condition coverage and response assessment, engine-vehicle interface definition, and abort system performance verification and validation are also discussed.

Santi, Louis M.↗
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 NTRS2020

Hawai'i Water Resources: Utilizing NASA Earth Observations to Assess Ocean Conditions Leading to the Spread of the Nuisance Red Algae (Chondria tumulosa) in Papahānaumokuākea Marine National Monument, Hawai’i

Chondria tumulosa, a newly discovered red alga, was observed in low abundance in 2016 but has since proliferated and is now smothering and decimating vast expanse of coral reefs in Manawai, located in Papahānaumokuākea Marine National Monument (PMNM). If the spread persists, the outbreak of this cryptogenic species could potentially cause region-wide ecosystem degradation. In coordination with the U.S. Fish and Wildlife Service, Marine National Monuments of the Pacific and the National Oceanographic and Atmospheric Administration (NOAA) Office of National Marine Sanctuaries’ Papahānaumokuākea Marine National Monument, this project created a tool to analyze oceanographic conditions (sea surface temperature (SST), chlorophyll-a, water velocity, salinity, turbidity) across the Monument that could potentially be driving the algal spread. The Google Earth Engine tool enabled the partners to visualize oceanographic conditions and gather time-series graphs utilizing Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Colour Instrument (OLCI), Hybrid Coordinate Ocean Model (HYCOM) and NOAA's Climate Data Record in a user-friendly interface. The team used in situ SST data from subsurface temperature recorders provided by the partners to validate the tool's accuracy. Preliminary statistical analysis of MODIS data found warming trends in SST in Manawai as well as increased chlorophyll-a levels during the summer months in contrast to the control (non-infected) Lalo atoll. The tool did not aim to classify algal presence due to limited availability of higher resolution satellite imagery but instead enabled PMNM managers to monitor conditions that may be conducive to algal growth around the monument to make informed decisions and mitigation practices.

DEVELOP Project Summary↗
DOE OSTI2022

Wind turbine gearbox fault prognosis using high-frequency SCADA data

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

17 WIND ENERGY↗
DOE OSTI2020

The Use of Long-wave Infrared Cameras for Hazardous Waste Remediation - 20457

For over fifty years, infrared cameras have been used in military applications, nondestructive testing, condition monitoring, and predictive maintenance. As infrared cameras continue to become more sophisticated and less expensive, they are providing value in an ever-increasing variety of unique applications, including hazardous waste remediation. This paper describes several instances of how infrared cameras have been used within the US Department of Defense and the US Department of Energy in support of waste remediation projects, including the author's recent use of an infrared camera in support of the Calcine Retrieval Project at the Idaho National Laboratory. Infrared cameras provide images of infrared radiation, or heat energy, which is otherwise invisible to the unaided eye. Infrared radiation is part of the electromagnetic spectrum, which includes visible light. But unlike visible light, infrared has wavelengths longer than the human eye can detect. Infrared is emitted by everything with a temperature above absolute zero (-273 deg. C, or -459 deg. F); the higher the temperature, the greater the infrared thermal radiation, or heat, that is emitted. Even objects that feel cold to us, like ice, emit thermal radiation and can be imaged by infrared cameras. These cameras are typically used to look for abnormally hot or cold spots on a component or target area under normal operating conditions. The method provides a rapid, wide-area, noncontact technique for identifying problems associated with a temperature differential. All infrared cameras can provide qualitative thermal information by displaying relative differences in temperatures within a two-dimensional image. More expensive infrared cameras can also provide quantitative information where an absolute temperature value is assigned to each pixel associated with the displayed two-dimensional image. Proper camera calibration and a solid understanding of heat transfer and thermography techniques are required when using an infrared camera to obtain quantitative information. Case studies outlined in this paper include the rapid, non-intrusive detection of hazardous decontamination solution within one-ton shipping containers at Pine Bluff Arsenal, the non-intrusive identification of residual elemental sodium within the cooling loops of the Experimental Breeder Reactor II (EBR-II) reactor at the Idaho National Laboratory, process monitoring of heat exchanger melt-and-drain efforts during EBR-II decommissioning, and the remote detection of internal steel supports within calcine storage bins prior to bin penetration. For each use of thermography, the author describes a summary of the waste remediation effort, the infrared camera used, the thermal imaging technique employed, and the results obtained. The paper concludes with a discussion on common mistakes to avoid for similar applications of thermography. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Acoustic diagnostic system and method for superconducting devices operating in gas or liquid

A novel system and method for detecting a quench of a superconducting conductor and detecting abnormal behavior of a superconducting conductor using acoustic sensor technology in the coolant of a superconducting cable and/or magnet is disclosed. This system and method is not only limited to use for superconductors, but also may be used for any device disposed in liquid and gas. Acoustic sensors are installed along a coolant space of a superconducting conductor and monitor coolant conditions. By monitoring acoustic changes, temperature changes or coolant flow disruption can be detected very quickly by an acoustic sensor array. By disposition of the acoustic sensor array in a coolant flow channel, the acoustic sensor system can quickly detect a local condition, such as the thermal status (temperature) of a superconducting cable and magnet with precise spatial resolution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Temperature Dependence of FDR Response for Thermally Aged Low-Voltage Cables

Frequency Domain Reflectometry (FDR) has attracted significant attention for use in nuclear power plants (NPPs) for non-destructive cable health monitoring. However, cable aging detection using FDR faces challenges due to its high sensitivity to environmental factors such as temperature, vibrations, proximity to other cables, and so on. This work aims to evaluate the influence of measurement temperature on the FDR reflected signal in a low voltage cable undergoing accelerated thermal aging. A 100 ft long multi-core low-voltage unshielded power cable insulated with flame retardant ethylene propylene rubber (FR-EPR) and covered by a chloro-polyethylene (CPE) jacket was selected for this study. The cable was energized during aging by a 480 VAC 3-phase motor, and a 30 ft mid-section of this cable was routed through an air circulating oven held at 140 °C for up to an effective aging time of 62 days. FDR measurements were taken periodically with the oven on (at 140 °C) and with the oven off (at 22 °C). A comparative analysis of data collected at both temperatures showed that the FDR response was strongly dependent on measurement temperature. FDR measurements at ambient temperature showed large peaks corresponding to impedance changes in the aged section of the cable after 3 days of aging. These peaks continued to rise steadily with increasing aging time. However, for measurements taken at 140 °C, slowly rising peaks in the oven region (aged section) were observed only after a lead time of 23 days of aging. This work highlights the importance of measurement temperature on the performance of FDR as a condition monitoring tool for aging cables.

Sriraman, Aishwarya↗
DOE OSTI2016

Technical Condition of Earthen Dams at Nizhegorodskaya HPP After 60 Years of Operation

The complex of hydraulic engineering installations includes five earthen dams. All facilities are of Class I. The diagnostic parameters of dam operation and technical condition monitored via in-situ observation for seepage mode, seepage strength, settlement, static stability, etc. generally meet design assumptions and safety criteria. Despite individual defects and signs of “aging” identified over the period of earthen dam operation, these are currently in a satisfactory and operable state. The long, 60-year operating period of the dams with no serious overhauls or emergencies has demonstrated the high reliability of the facilities, the correctness of the design and technological solutions adopted and implemented in the construction, and the high professional level of their operation in close cooperation with science.

42 ENGINEERING↗
DOE OSTI2024

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

Vibration-based condition monitoring techniques are widely used for diagnosing faults in rotating machines. These techniques are implemented in the time domain, the frequency domain, or both. However, the composite and noisy nature of the raw data collected requires a preprocessing stage such as filtering and decomposition using in-depth processing techniques. Moreover, these methods require good frequency resolution and involve examining a broad frequency range to discern both healthy and faulty cases. In this work, we introduce a simple and fast diagnostic scheme for wind turbine gear teeth wear based on time domain analysis. The proposed method is based on the local minima interpolation of a filtered version of the vibration signal following time synchronous averaging (TSA) technique. Given tachometer signal, the TSA of the vibration data is performed using MTALAB software. Then, local minima of the filtered signal are interpolated using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function. The variance of the interpolated curve built a gear fault index. The derived fault index resulting of the proposed technique allows a substantial distinction between the healthy and faulty cases. Its efficiency is validated using 10 real-world datasets of vibration stemmed from a wind turbine planetary gearbox. The proposed method boasts a low computation time and ease of interpretation, specifically beneficial for gearbox fault diagnosis purposes.

fault diagnosis↗
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Modeling, control, and dynamic performance analysis of a reverse osmosis desalination plant integrated within hybrid energy systems

An RO (reverse osmosis) desalination plant is proposed as an effective, FLR (flexible load resource) to be integrated into HES (hybrid energy systems) to support various types of ancillary services to the electric grid, under variable operating conditions. To study the dynamic (transient) analysis of such system, among the various unit operations within HES, special attention is given here to the detailed dynamic modeling and control design of RO desalination process with a spiral-wound membrane module. The model incorporates key physical phenomena that have been investigated individually into a dynamic integrated model framework. In particular, the solution-diffusion model modified with the concentration polarization theory is applied to predict RO performance over a large range of operating conditions. Simulation results involving several case studies suggest that an RO desalination plant, acting as a FLR, can provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess electrical energy. Here, the incorporation of additional commodity (fresh water) produced from a FLR allows a broader range of HES operations for maximizing overall system performance and profitability. For the purpose of assessing the incorporation of health assessment into process operations, an online condition monitoring approach for RO membrane fouling supervision is addressed in the case study presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗
DOE OSTI2018

Results on radiation tolerance of diamond detectors

In sight of the luminosity increase of the High Luminosity-LHC (HL-LHC), most experiments at the CERN Large Hadron Collider (LHC) are planning upgrades for their innermost layers in the next 5–10 years. These upgrades will require more radiation tolerant technologies than exist today. Usage of Chemical Vapor Deposition (CVD) diamond as detector material is one of the potentially interesting technologies for the upgrade. CVD diamond has been used extensively in the beam condition monitors of BaBar, Belle, CDF and all LHC experiments. Measurements of the radiation tolerance of the highest quality polycrystalline CVD material for a range of proton energies, pions and neutrons obtained with this material are presented. In addition, new results on the evolution of various semiconductor parameters as a function of the dose rate are described.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Estimation of cutting tool wear using an elastomeric tactile sensor

Machining performance of cutting tools and part quality are affected by the geometric condition of the cutting edge, which is influenced by thermomechanical loads experienced during the process. Tool condition monitoring (TCM) systems provide insight for timely replacement of cutting tools. However, existing TCM systems are expensive and require specialized equipment or sensors, hindering widespread adoption. A novel TCM system is developed herein using an elastomeric tactile sensor. Sensor images of the cutting edge are used to quantify wear using two distinct algorithms. In the first algorithm, the unworn and worn edges are identified based on Canny edge detect. In the second, the unworn edge is identified using edge detection while the region of wear is identified using a relative intensity method. In both cases, the maximum wear width is calculated based on an experimentally determined pixel to-physical distance scale. The TCM system is first used to estimate flank wear on a solid carbide helical end mill before evaluating its robustness by employing it to estimate insert wear of an indexable helical end mill. Measurements are also performed manually using an optical microscope and a high-resolution focus variation microscope for verification. The novel technique estimates flank wear in the solid carbide tool with a high accuracy of 98%. Larger discrepancies are observed for the inserts, however, with overlapping uncertainties. In conclusion, the technique shows promise in adaptability, automation, and closed loop control of machine tools.

Machining↗