Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Monitoring”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

NREL’s Wind Turbine Drivetrain Condition Monitoring and Wind Plant Operation and Maintenance Research During the 2010s: A US Land-Based Perspective

The wind industry has seen tremendous growth during the past two decades, with the global cumulative installation capacity reaching more than 650 gigawatts by the end of 2019. Despite performance and reliability improvements of utility-scale wind turbines over the years, the industry still experiences premature component failures, leading to increased operation and maintenance (O&M) costs. Among various turbine components, gearboxes—and, more broadly, drivetrains—have shown to be costly to maintain throughout the design life of a wind turbine. The problem of premature component failure is industry wide. As early as 2007, the US Department of Energy (DOE) started to address this challenge through the National Renewable Energy Laboratory’s (NREL’s) reliability initiative that first focused on gearboxes and more recently expanded to entire drivetrains. The wind turbine drivetrain condition monitoring and wind plant O&M research that is the subject of this paper is part of the NREL initiative and includes a few research and development (R&D) activities conducted during the 2010s. These activities included technology evaluation during the first few years; novel monitoring technique investigation (specifically, compact filter analysis) during the middle years; and data and physics domain modeling for fault detection and prediction in recent years. A high-level summary of these activities is provided in this paper along with some key observations from each activity. Most of the work discussed has been published and can be referred to for more information. They reflect the expected evolution of wind turbine condition monitoring and O&M in the US market—primarily, a land-based perspective. In addition, we have identified several R&D opportunities that can be picked up by the research community to help industry advance in related areas, making wind power more cost competitive in the future.

17 WIND ENERGY↗
DOE OSTI2023

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Pressure-based process monitoring of direct-ink write material extrusion additive manufacturing

As additive manufacturing (AM) has become a reliable method for creating complex and unique hardware rapidly, the quality assurance of printed parts remains a priority. In situ process monitoring offers an approach for performing quality control while simultaneously minimizing post-production inspection. For extrusion printing processes, direct linkages between extrusion pressure fluctuations and print defects can be established by integrating pressure sensors onto the print head. In this work, the sensitivity of process monitoring is tested using engineered spherical defects. Pressure and force sensors located near an ink reservoir and just before the nozzle are shown to assist in identification of air bubbles, changes in height between the print head and build surface, clogs, and particle aggregates with a detection threshold of 60–70% of the nozzle diameter. Visual evidence of printed bead distortion is quantified using optical image analysis and correlated to pressure measurements. Importantly, this methodology provides an ability to monitor the quality of AM parts produced by extrusion printing methods and can be accomplished using commonly available pressure-sensing equipment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Monitoring of corrosion effects in pipes with multi-mode acoustic signals

Current commercial long-range guided wave inspection and monitoring technologies for pipes usually require establishment of certain specific acoustic modes for wave propagation and use a large number of sensors in a certain geometric arrangement (for example, uniformly-spaced arrays of sensors on a collar, placed around a pipe). This paper, in contrast, presents a long-range corrosion detection method in pipes based on multi-mode guided acoustic waves, generated and received using simple, single-element sensors. In this technique, available guided wave modes in any interrogated zone of the structure are automatically down-selected by the medium, and changes in energy distribution over the selected modes are analyzed through signal processing to monitor material loss or addition. Here, to investigate the feasibility of the proposed method, we have performed both experimental and numerical simulation studies on 50.8-mm (2-in)-diameter schedule-40 carbon steel pipes. In these studies, a linear chirp signal is injected in compression mode, perpendicular to the surface of the pipe, generating multi-mode, multi-directional guided waves that travel within the pipe wall. Corrosion effects in the pipes are simulated numerically and experimentally. To reduce errors in the measurement technique due to changes in the local or global temperature of the measured media, a universal temperature compensation algorithm is introduced. The experimental results show good agreement with simulation results, indicating that the proposed multi-mode wave generation and detection in a pipe could be used for long-range detection and monitoring of corrosion effects, such as pipe wall losses or solid material deposits.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Google search patterns monitoring the daily health impact of heatwaves in England: How do the findings compare to established syndromic surveillance systems from 2013 to 2017?

Highlights: • Heat health-related Google search term frequency increased during heatwave events. • Google data correlated well with validated syndromic heat health indicators. • A similar timeliness was seen between daily Google and syndromic data in England. • Google search data could be of benefit in monitoring heat-related morbidity. One of the implications of climate change is a predicted increase in frequent and severe heatwaves. The impact of heatwaves on the health of the population is captured through real-time syndromic healthcare surveillance systems monitored daily in England during the summer months. Internet search data could potentially provide improved timeliness and help to assess the wider population health impact of heat by capturing a population sub-group who are symptomatic but do not seek healthcare. A retrospective observational study was carried out from June 2013 to September 2017 in England to compare daily trends in validated syndromic surveillance heat-related morbidity indicators against symptom-based heatwave related Google search terms. The degree of correlation was determined with Spearman correlation coefficients and lag assessment was carried out to determine timeliness. Daily increases in frequency in Google search terms during heatwave events correlated well with validated syndromic indicators. Correlation coefficients between search term frequency and syndromic indicators from 2013 to 2017 were highest with the telehealth service NHS 111 (range of 0.684–0.900 by search term). Lag analysis revealed a similar timeliness between the data sources, suggesting Google data did not provide a delayed or earlier signal in the context of England's syndromic surveillance systems. This work highlights the potential benefits for countries which lack established public health surveillance systems to monitor heat-related morbidity and the use of internet search data to assess the wider population health impact of exposure to heat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Statistical analysis of pulsed-neutron well logs in monitoring injected carbon dioxide

Pulsed-neutron well logs (PNLs) were acquired to monitor CO 2 storage associated with enhanced oil recovery. This work quantifies the precision of repeat PNLs using data from four wells and 15 repeat PNLs. Root-mean-square (RMS) precision for the repeat PNLs was less than 3%, indicating good agreement between the baseline and repeat PNLs. Evaluations of scaled relative difference (Scaled-D) showed variation in precision among individual wells and formations. Analysis of false-positive rates (FPRs) across the entire data set showed that a Scaled-D threshold in sandstone formations of approximately ±8% resulted in a 1% FPR. These Scaled-D precision thresholds were used to estimate the value of CO 2 saturation able to be confidently distinguished from baseline. The detection limit for CO 2 is lowest for high-porosity formations filled with saline water and is highest for low-porosity formations filled with fresh water. Thus, detection of vertical out-of-zone CO 2 migration using repeat PNLs is a function of instrument precision, petrophysical properties, and hydrology, all of which must be taken into account as part of the monitoring program. Here, the results of this work provide insight into how PNLs may be included within monitoring plans to detect vertical out-of-zone CO 2 migration along a wellbore or instances of wellbore failure and provide a quantitative basis for establishing detection limits of repeat PNLs to distinguish change from baseline conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Risk-based Post Injection Site Care and Monitoring for Commercial-Scale Carbon Storage: Reevaluation of the FutureGen 2.0 Site using NRAP-Open-IAM and DREAM

Modeling was conducted to assess the effectiveness of the NRAP toolset, specifically NRAP-IAM-CS v2 and DREAM, to determine a risk-based PISC period and optimized monitoring network for a commercial-scale CO2 storage project. Realizations from NRAP-IAM-CS v2 revealed that leakage rates of both CO2 and brine were small, on the order of 10-5 kg/s, and occurred primarily during the injection phase in the Ironton-Galesville, the thief zone immediately overlying the injection reservoir. Using this information to design an optimized monitoring well network eliminated one of the two originally planned above zone monitoring wells, resulting a substantial cost reduction for the project. Perhaps the most significant finding from this effort is that NRAP-IAM-CS v2 can be used to define a risk-based, and substantially shorter, PISC period for the site. NRAP-IAM-CS v2 realizations indicate that the majority of risk of endagerment to USDWs decreases within the first 5 years after CO2 injection ends. Doubling this timeframe would still lead to a net PISC period reduction of 40-years and an operational cost reduction in excess of $50M for the project. This study serves as a first-of-its-kind effort to apply NRAP tools to evaluate components of a commercial-scale Class VI UIC permit and provides a foundation for broader application and adoption by the CCS community.

system model, integrated assessment, reservoir, aq↗
DOE OSTI2020

Geophysical monitoring using active seismic techniques at the Citronelle Alabama CO 2 storage demonstration site

Between August 2012 and September 2014, about 114,000 metric tonnes of CO 2 was captured from the coal-fired Plant Barry Power Station at Bucks Alabama and injected into the Paluxy Formation above the oil pool in the southeast unit of the Citronelle Oilfield. Various monitoring methods were deployed at land surface and in project wells to measure system performance, comply with permit requirements and test new and innovative monitoring tools. The monitoring program relied heavily on active seismic methods for subsurface imaging of geologic structure and time-lapse seismic techniques to track the CO 2 migration in the injection interval. Both conventional geophone/hydrophone and fiber-optic based Distributed Acoustic Sensing (DAS) arrays were deployed and tested, allowing a side by side comparison of the equipment and techniques. Geophysical imaging of the subsurface was successful using DAS in the offset vertical seismic profile (OVSP) survey configuration. A high resolution OVSP image of the subsurface was obtained in 2014 with DAS, which exceeded project expectations in comparison to a lower resolution image obtained in 2012 using a conventional 80-level geophone array. A time-lapse image of the redistribution of CO 2 after injection ended in September 2014 was obtained with two DAS OVSP surveys from June 2014 and December 2015, thus successfully demonstrating its proof-of-concept. Unfortunately, a pre-injection baseline survey with DAS, which was in its initial stage of technology development in 2012, did not have sufficient quality for use, making it difficult to interpret the acquired DAS time-lapse difference. Additional research in this area has since demonstrated the utility of time-lapse DAS OVSP. DAS data were also acquired during a cross-well seismic survey conducted in 2014. Unfortunately, the DAS technique was not success in the cross-well survey configuration because the system noise level was too high in the crosswell frequency output range (100–1200 Hz) of the piezoelectric source (increasing by a factor of ten compared to VSP frequency band). Additionally, the cross-well geometry causes sub-horizontal (broadside) incidence on the vertical DAS fiber cable, which is known to be problematic. Current research is focused on improving the DAS cable response to broadside acoustic energy. Time-lapse seismic surveys using commercially available conventional arrays were also acquired. In contrast to the DAS acquired data, the cross-well seismic results obtained with the conventional array was highly successful and clearly showed the CO 2 remained in zone at the end of injection. Time-lapse differencing of the OSVP surveys acquired with the conventional arrays proved to be inconclusive. Finally, changes in wellbore conditions between surveys and unavoidable changes in equipment (the array used for the baseline survey was retired) affected data quality, making it difficult to interpret the OVSP results.

58 GEOSCIENCES↗
DOE OSTI2021

Efficient marine environmental characterisation to support monitoring of geological CO 2 storage

Carbon capture and storage is key for mitigating greenhouse gas emissions, and offshore geological formations provide vast CO 2 storage potential. Monitoring of sub-seabed CO 2 storage sites requires that anomalies signifying a loss of containment be detected, and if attributed to storage, quantified and their impact assessed. However, monitoring at or above the seabed is only useful if one can reliably differentiate abnormal signals from natural variability. Baseline acquisition is the default option for describing the natural state, however we argue that a comprehensive baseline assessment is likely expensive and time-bound, given the multi-decadal nature of CCS operations and the dynamic heterogeneity of the marine environment. We present an outline of the elements comprising an efficient marine environmental baseline to support offshore monitoring. We demonstrate that many of these elements can be derived from pre-existing and ongoing sources, not necessarily related to CCS project development. We argue that a sufficient baseline can be achieved by identifying key emergent properties of the system rather than assembling an extensive description of the physical, chemical and biological states. Further, that contemporary comparisons between impacted and non-impacted sites are likely to be as valuable as before and after comparisons. Furthermore, as these emergent properties may be nuanced between sites and seasons and comparative studies need to be validated by the careful choice of reference site, a site-specific understanding of the scales of heterogeneity will be an invaluable component of a baseline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Unsupervised learning from three-component accelerometer data to monitor the spatiotemporal evolution of meso-scale hydraulic fractures

Enhanced geothermal systems can provide a substantial share of the global energy demand. There exist several hurdles in the engineering implementations of such geothermal systems. One such hurdle is the accurate monitoring of the fracture networks created in subsurface through hydraulic stimulation of these systems. Micro seismicity associated with the stimulation is the primary means to locate the event hypocenters for estimating the stimulated rock volume. Existing methods for location the hypocenters are restricted to only the highest amplitude impulsive signals that are simultaneously detected on several sensors. Consequently, a large portion (usually ~99%) of the measurements are left unused. In this paper, an unsupervised manifold-approximation followed by clustering of 3-component accelerometer data is used to analyze the seismicity recorded on a monitoring well. With this method, a larger portion of the measured signal is used for the monitoring of the hydraulic fracture network. We analyze the EGS Collab experiment 1 microseismic data, recorded at the Sanford Underground Research Facility, South Dakota. Using the data from a single three-component accelerometer, the polarization features viz. Azimuth, incidence, rectilinearity, and planarity are used as inputs for the unsupervised manifold approximation followed by clustering. Our study shows that density-based clusters in the projected 3D space correspond to distinct types of hydraulically fractured zones around the injection point. Finally, we show that the temporal evolution of these clusters can be used to track fracture creation and propagation.

58 GEOSCIENCES↗
DOE OSTI2012

Feasibility of Electromagnetic Transponder Use to Monitor Inter- and Intrafractional Motion in Locally Advanced Pancreatic Cancer Patients

Purpose: The primary objective of this study was to determine the feasibility of electromagnetic transponder implantation in patients with locally advanced unresectable pancreatic cancer. Secondarily, the use of transponders to monitor inter- and intrafractional motion, and the efficacy of breath holding for limiting target motion, were examined. Methods and Materials: During routine screening laparoscopy, 5 patients without metastatic disease were implanted with transponders peri-tumorally. The Calypso System's localization and tracking modes were used to monitor inter- and intrafractional motion, respectively. Intrafractional motion, with and without breath holding, was also examined using Calypso tracking mode. Results: Transponder implantation was well tolerated in all patients, with minimal migration, aside from 1 patient who expulsed a single transponder. Interfractional motion based on mean shifts from setup using tattoos/orthogonal imaging to transponder based localization from 164 treatments was significant in all dimensions. Mean shift (in millimeters), followed by the standard deviation and p value, were as follows: X-axis: 4.5 mm (1.0, p = 0.01); Y axis: 6.4 mm (1.9, p = 0.03); and Z-axis 3.9 mm (0.6, p = 0.002). Mean intrafractional motion was also found to be significant in all directions: superior, 7.2 mm (0.9, p = 0.01); inferior, 11.9 mm (0.9, p < 0.01); anterior: 4.9 mm (0.5, p = 0.01); posterior, 2.9 mm (0.5, p = 0.02); left, 2.2 mm (0.4, p = 0.02); and right, 3.1 mm (0.6, p = 0.04). Breath holding during treatment significantly decreased tumor motion in all directions. Conclusions: Electromagnetic transponder implantation appears to be safe and effective for monitoring inter- and intrafractional motion. Based on these results a larger clinical trial is underway.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Online Image-based Monitoring of Soft-tissue Displacements for Radiation Therapy of the Prostate

Purpose: Emerging prolonged, hypofractionated radiotherapy regimens rely on high-dose conformality to minimize toxicity and thus can benefit from image guidance systems that continuously monitor target position during beam delivery. To address this need we previously developed, as a potential add-on device for existing linear accelerators, a novel telerobotic ultrasound system capable of real-time, soft-tissue imaging. Expanding on this capability, the aim of this work was to develop and characterize an image-based technique for real-time detection of prostate displacements. Methods and Materials: Image processing techniques were implemented on spatially localized ultrasound images to generate two parameters representing prostate displacements in real time. In a phantom and five volunteers, soft-tissue targets were continuously imaged with a customized robotic manipulator while recording the two tissue displacement parameters (TDPs). Variations of the TDPs in the absence of tissue displacements were evaluated, as was the sensitivity of the TDPs to prostate translations and rotations. Robustness of the approach to probe force was also investigated. Results: With 95% confidence, the proposed method detected in vivo prostate displacements before they exceeded 2.3, 2.5, and 2.8 mm in anteroposterior, superoinferior, and mediolateral directions. Prostate pitch was detected before exceeding 4.7 Degree-Sign at 95% confidence. Total system time lag averaged 173 ms, mostly limited by ultrasound acquisition rate. False positives (FPs) (FP) in the absence of displacements did not exceed 1.5 FP events per 10 min of continuous in vivo imaging time. Conclusions: The feasibility of using telerobotic ultrasound for real-time, soft-tissue-based monitoring of target displacements was confirmed in vivo. Such monitoring has the potential to detect small clinically relevant intrafractional variations of the prostate position during beam delivery.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Review of in situ process monitoring for metal hybrid directed energy deposition

Hybrid manufacturing, or the combination of additive and subtractive manufacturing within a single build volume is transforming the way products are being fabricated. Additive manufacturing confers unprecedented freedom of design and reduced material usage while enabling serial customization. Subtractive manufacturing provides superior surface finish and improved dimensional accuracies. Interwoven, these two digital manufacturing paradigms are enabling the rapid manufacturing of complex, highly accurate, and customized geometries in a diversity of high-performance alloys. In situ monitoring, heavily relied upon in either additive or subtractive, becomes even more crucial with the interplay of the two processes in a single combined build sequence. Moreover, challenges that do not exist in either process alone can now have a dramatic impact on final part quality: (1) a large amount of heat is generated during additive manufacturing deposition, which is primarily dissipated into the machine tooling and impacts accuracy by deforming the material and causing misaligned machining; (2) microstructure, mechanical properties, and residual stresses are the result of the complex thermal histories generated by additive manufacturing and can impact subsequent cutting performance. This comprehensive review considers previous research in monitoring and providing closed-loop control in both additive and subtractive manufacturing separately and then considers the implications of the effectiveness of these monitoring techniques when additive and subtractive processes are integrated together.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2014

The Detection of Reactor Antineutrinos for Reactor Core Monitoring: an Overview

There have been new developments in the field of applied neutrino physics during the last decade. The International Atomic Energy Agency (IAEA) has expressed interest in the potentialities of antineutrino detection as a new tool for reactor monitoring and has created an ad hoc Working Group in late 2010 to follow the associated research and development. Several research projects are ongoing around the world to build antineutrino detectors dedicated to reactor monitoring, to search for and develop innovative detection techniques, or to simulate and study the characteristics of the antineutrino emission of actual and innovative nuclear reactor designs. We give, in these proceedings, an overview of the relevant properties of antineutrinos, the possibilities of and limitations on their detection, and the status of the development of a variety of compact antineutrino detectors for reactor monitoring.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Advanced focusing system for secondary electrons in a bunch shape monitor

Non-destructive measurements of longitudinal beam profile in non-relativistic hadron accelerators are usually performed with Bunch Shape Monitors that utilize secondary electron emission to reproduce the shape of a primary ion beam. The existing devices, however, have poor electron collection efficiency from the wire and are limited to one dimensional measurements of the phase coordinate. Here, we present the development of a bunch shape monitor with improved performance for proton beams to be used in the Spallation Neutron Source accelerator. This improvement was achieved by adding focusing optics between the wire and the entrance slit, which will also allow measurements over a much higher dynamic range. Here we present the design, simulation and experimental test results of the developed new electron beam guidance system for the existing SNS bunch shape monitor that allowed significant improvement in the collection efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗