Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*

Project Title: Add title here

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Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

Data availability statement: If your data is not linked on figshare directly, provide links to where it is being hosted here (i.e., Open Science Framework, Github, etc.). If your data is not going to be made publicly available, please provide details here as to the conditions under which interested individuals could gain access to the data and how to go about doing so.

Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

During the course of this experimental study

you-lab/foundation-model-data-experimental dataset hosted on Hugging Face and contributed by the HF Datasets community

This resource contains the experimental data that was included in tecplot input files but in matlab files. dba1_cp has all the results is dimensioned (7,2) first dimension is 1-7 for each span station 2nd dimension is 1 for upper surface, 2 for lower surface. dba1_cp(ispan,isurf).x are the x/c locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).y are the eta locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).cp are the pressures at span station (ispan) and upper(isurf=1) or lower(isurf=2) Unsteady CP is dimensioned with 4 columns 1st column, real 2nd column, imaginary 3rd column, magnitude 4th column, phase, deg M,Re and other pertinent variables are included as variables and also included in casedata.M, etc

river map

The HIRENASD data produced by analyzing the experimental data is repeated on this website, for those who can not download the information in the zip format found on the primary Experimental Data page, or who wish to examine the plots of the data online.

This dataset contains the underling data for the papers:

M. Kuzay, A. Dogan, S. Yilmaz, O. Herkiloglu, A.S. Atalay, C. Yilmaz, E. Demirel, Retrofitting of an air-cooled data center for energy efficiency, Case Studies in Thermal Engineering (2022), 36, 102228. https://doi.org/10.1016/j.csite.2022.102228.

M. Kuzay, A. Dogan, S. Yilmaz, O. Herkiloglu, A.S. Atalay, C. Yilmaz, E. Demirel, Numerical and experimental dataset for an air-cooled data center, Data in Brief (2022).

All cases were prepared using OpenFOAM 8 for the simulation of flow and thermal structures inside the data center for both previous and retrofitted designs.

previousDesign.tar.xz: OpenFOAM files and scripts for the simulation of flow and thermal structures in the previous data center design.

retrofittedDesign.tar.xz: OpenFOAM files and scripts for the simulation of flow and thermal structures in the retrofitted data center design.

experimentalScenarios.tar.xz: OpenFOAM files and scripts for the simulation of flow and thermal structures under the same thermal conditions as in the experimental studies.

data.tar.xz: Temperature data obtained from the experimental studies conducted for previous and retrofitted designs.

Experimental data presented in the paper

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

The mission of the High Throughput Experimental Materials Database (HTEM DB) is to enable discovery of new materials with useful properties by releasing large amounts of high-quality experimental data to public. The HTEM DB contains information about materials obtained from high-throughput experiments at the National Renewable Energy Laboratory (NREL).

This data set contains simultaneous stereoscopic Particle Image Velocimetry and LIF surface measurements, as well as probe data for the experiment described in the article titled "Experimental study of the mutual interactions between waves and tailored turbulence". This work is partially funded by the European Union (see funding information): Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

and its exploitation to evaluate relative separation andangular displacement between coils. This innovative measurement technique explores the bimodal resonant phenomena observedbetween two coil designs - solenoid and planar coil geometries. The proposed sensors are evaluated against first-order analytical

Experimental data of the paper "ADARW Training Optimization Algorithm for Deep Learning Model of Marine Target Detection Based on SAR"

Data includes three parts: "Model", "Original_data" and "Result_data"

🇪🇺 유럽연합

This document is part of a series of reports describing experimental property measurements completed at the National Institute for Petroleum and Energy Research (NIPER) in Bartlesville, Oklahoma, in the 1980s and 1990s. Members of the Bartlesville Thermodynamics Group included William D. "Bill" Good, William V. "Bill" Steele, Bruce E. Gammon, Norris K. Smith, Stephen E. Knipmeyer, An "Andy" Nguyen, Timothy D. Klots, I. A. "Alex" Hossenlopp, Aaron P. Rau, William B. Collier, John F. Messerly, Ann G. Osborn, Susan Lee-Bechtold, Donald G. Archer, Ian R. Tasker, Allan B. Cowell, Michael M. Strube, and the author of this report. A summary of the measurements reported here is given in Table 1, together with a list of experimental results that were reported previously and used in the generation of the derived properties.

This file compiles the different datasets used and analysis made in the paper "Visual Continuous Time Preferences". Both RStudio and Stata were used for the analysis. The first was used for descriptive statistics and graphs, the second for regressions. We join the datasets for both analysis.

"Analysis VCTP - RStudio.R" is the RStudio analysis. "Analysis VCTP - Stata.do" is the Stata analysis.

The RStudio datasets are: "data_Seville.xlsx" is the dataset of observations. "FormularioEng.xlsx" is the dataset of control variables.

The Stata datasets are: "data_Seville_Stata.dta" is the dataset of observations. "FormularioEng.dta" is the dataset of control variables

Additionally, the experimental instructions of the six experimental conditions are also available: "Hypothetical MPL-VCTP.pdf" is the instructions and task for hypothetical payment and MPL answered before VCTP. "Hypothetical VCTP-MPL.pdf" is the instructions and task for hypothetical payment and VCTP answered before MPL. "OneTenth MPL-VCTP.pdf" is the instructions and task for BRIS payment and MPL answered before VCTP. "OneTenth VCTP-MPL.pdf" is the instructions and task for BRIS payment and VCTP answered before MPL. "Real MPL-VCTP.pdf" is the instructions and task for real payment and VCTP answered before MPL. "Real VCTP-MPL.pdf" is the instructions and task for real payment and VCTP answered before MPL.

Datasets and software to reproduce the results of the research.

Description This repository includes the experiment results, source code, and test data for Three Cs risk inference, using the CIRO (COVID-19 Infection Risk Ontology) and HermiT.

results.csv: The results of the experiment. test_cases.csv: The test cases of the experiment. CIRO.owl: The COVID-19 Infection Risk Ontology. test_main.py: The source code of the experiment. Requirements:

Python 3 Owlready2 rdflib

Microsoft Excel.