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6 results for “Ambient vibrations”
Data set from ambient vibration monitoring and static loading of a steel stringer bridge subject to imposed damage
<p>This data set contains structural response measurements acquired from the Route 345 Bridge over Big Sucker Brook in Waddington, NY prior to demolition and replacement. This steel stringer bridge was instrumented with dual-axis accelerometers and strain transducers and response measurements were obtained prior to and following mechanically imposed damages. Accelerometer measurements were obtained under vehicle passes and strain measurements were acquired during static loading of the span with a truck positioned at three prescribed locations. All measurement data is contained in a single h5-file (hierarchical data format version 5). The data is shared with the intent of promoting the advancement of structural health monitoring and vibration-based damage detection. </p> <p><strong>Version 2 Note: </strong>This version corrects the strain measurement data. Due to an index counter error in the script that compiled the strain measurement data into the h5-file, the strain measurement data in the Version 1 data were incorrectly sourced from a single scenario. The strain measurements are corrected in this new version. No other changes were made in the h5-file.</p>
Ambient vibration test of wind turbine blade in OWI-lab's Large Climate Chamber
<p><strong>Ambient vibration test of wind turbine of wind turbine blade in OWI-lab's Large Climate Chamber</strong></p> <p>Selected data from the large scale icing experiment as conducted in OWI-lab's large climate chamber on 15/11/2022. Results were presented during Eurodyn 2023 in :"Large scale test of vibration based icing detection for wind turbines", Weijtjens et.al. </p> <p><em>Data is (summarized, more details are given below):</em></p> <p>- 24 Ten minute acceleration data files collected (MO04_acceleration_YYYYmmdd_HHMMSS.csv) <br> - Pictures during the experiment timestamped (local time: UTC+1)<br> - Temperature measurements of the climate chamber's inflow temperatures<br> - Modal parameter results for X and Z direction </p> <p>All times are in UTC unless mentioned otherwise.</p> <p><strong>Measurement concept</strong></p> <p>The measurement data is collected during an experiment as conducted as part of the <a href="https://www.sirris.be/nl/joint-project/fighting-icing">COOCK fighting icing </a> project led by Sirris. In the OWI-lab climate chamber a wind turbine blade was subjected to icing conditions. The test comprises the collection of ambient vibration data using three tri-axial accelerometers installed on the blade. During the day the blade is cooled and cold water is sprayed on the blade to simulate the growth of ice on the blade. The steps of the experiment are:</p> <pre><code>2022-11-15 10:04:00+00:00: Start cooling to -10°C 2022-11-15 11:23:00+00:00: Start spray 2022-11-15 12:13:00+00:00: Accelerate spray 2022-11-15 12:49:00+00:00: End of spray 2022-11-15 13:13:00+00:00: Start heating 2022-11-15 14:14:00+00:00: Start cooling to -10°C 2022-11-15 15:03:00+00:00: Start spray 2022-11-15 15:43:00+00:00: End of spray</code></pre> <p>For more information on the </p> <p><strong>Ten minute acceleration data</strong></p> <p>Twentyfour ten minute samples of the 3 accelerometers on the blade sampled at 250Hz. The data is two 2-hour blocks, one at night before the testing, the second 2 hour block is during the spraying.</p> <p>The 10.1m long blade was instrumented with three tri-axial MEMS accelerometers (Micromega IAC-UHRS-Ud-03, ±3g) on the suction side of the blade. In which the X-direction corresponded to the edgewise motion of the blade, the Z-direction to the flapwise direction and the Y-direction to the less relevant lengthwise motion. The three sensors were installed at approximately 1/4, 5/8 of the blade length and 130cm from the tip of the blade.</p> <p><strong>Modal parameter data</strong></p> <p>The resulting modal parameter data ( for the entire day of testing) , in both X and Z direction are provided in MO04_mpe_*_20221115.csv. The data has following shape:</p> <table> <thead> <tr> <th> </th> <th>mean_frequency</th> <th>std_frequency</th> <th>mean_damping</th> <th>std_damping</th> <th>size</th> <th>algorithm</th> <th>timestamp</th> </tr> </thead> <tbody> <tr> <th>0</th> <td>1.649219</td> <td>0.001952</td> <td>1.128411</td> <td>0.103419</td> <td>51</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>1</th> <td>2.028879</td> <td>0.000927</td> <td>0.432596</td> <td>0.068354</td> <td>60</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>2</th> <td>2.923999</td> <td>0.009472</td> <td>2.677156</td> <td>0.615895</td> <td>19</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>3</th> <td>3.779268</td> <td>0.003649</td> <td>3.462347</td> <td>0.715628</td> <td>7</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> <tr> <th>4</th> <td>5.786029</td> <td>0.005541</td> <td>0.539010</td> <td>0.261049</td> <td>7</td> <td>lscf</td> <td>2022-11-15 00:00:00+00:00</td> </tr> </tbody> </table> <p>In which `mean_frequency` and `std_frequency` are the cluster mean frequency, are the cluster std. frequency and cluster std. damping and the cluster size (size). The LCSF algorithm is used. The algorithm used is described in <a href="https://journals.sagepub.com/doi/abs/10.1177/1475921714556568?journalCode=shma">Source</a>.</p> <p>Note: multiple rows share one timestamp, this is because the algorithm can detect multiple modes per timestamp.</p> <p><strong>Temperature data</strong></p> <p>The inflow air temperatures are shared in a separate .csv files: ClimateChamber_20221115.csv</p> <p><strong>Pictures</strong></p> <p>Picture are collected during the test are shared. Each picture is timestamped in local time (UTC+1)</p> <p> </p>
GNSS data of ambient vibrations and artificial excitations of the Aaresteg bridge.
<p>GNSS Data:</p> <p>- Instrument: Java GrAnt-G3T antenna and Septentrio PolaRx receiver</p> <p>- sampling rate: 20 Hz</p> <p>- Bandwidth of loop filter: auto adjust</p> <p>- Date: 2021-09-27</p> <p>Location:</p> <p>Instrument was set up on the eastern railing of the Aaresteg bridge, 1.125 m height above the wooden planks. The bridge was excited by jumping, twisting, running, walking, impulse hammering and combinations thereof. </p>
Ambient vibrations of the San Frediano bell tower in Lucca
<table> <tbody> <tr> <th> </th> <td>The San Frediano bell tower in Lucca, dating back to the thirteenth century, is located in the historic center of Lucca and is about 50 m high. This dataset hosts the velocities recorded on the tower from 29-5-15 to 3-6-2015 under ambient vibrations. The velocities were recorded by four tri-axial seismic stations installed at different positions along the tower's height. Data have been acquired continuously with a frequency of 100 sp. The recordings hosted in the dataset are given in count and can be transformed to m/s using the constant 119*10^-9 /200. This constant allows for a good approximation of the velocity values in the frequency range of interest. For more information on the transfer function of the instruments, please get in touch with the Authors. Data were acquired in the MONSTER and the SOUL project frameworks, both funded by the "Fondazione Cassa di Risparmio di Lucca", whose contribution is gratefully acknowledged.</td> </tr> </tbody> </table>
Ambient vibrations of the Guinigi tower in Lucca
<table> <tbody> <tr> <th> <table> <tbody> <tr> <td> <p>The Guinigi Tower is located in the historic center of Lucca and dates back to the fourteenth century. The vibrations of the tower under ambient noise were continuously measured from June 25th 2021, to September 13th 2022. This dataset includes the recordings from 1st to 7th October 2021 acquired via two seismic stations (2898, 2897) SS45 by Sara Electronic Instruments. Each file contains a one-hour recording, as specified in the file name. The six columns in the file are 2898x, 2898y, 2898z, 2897x, 2897y, and 2897z. The sensor map defines the instruments' axes, their position and the tower's geometry. Measurements are expressed in m/s.</p> <p> </p> </td> </tr> </tbody> </table> </th> <td> </td> </tr> </tbody> </table>
Multi-modal Dataset of Human Activities of Daily Living with Ambient Audio, Vibration and Environmental Data
<pre>This dataset provides over 43000 samples of 25 different human activities (e.g. walking, opening/closing a door, sitting down, vacuum cleaning). Each sample is recorded by 5 sensor devices with multiple types of sensors. The main part is audio and vibration. Further metrics were recorded at a low frequency, and are: infrared array, light color, temperature, relative humidity, atmospheric pressure, air quality measure, volatile organic compounds and CO2 equivalent. The data was recorded in supervised sessions to label each sample. The recording environment consisted of a kitchen and dining room. Flawed samples were removed and the different metrics were synchronized, but no further processing or filtering of the data was performed. </pre>
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