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5 results for “bearing vibration”
The Politecnico di Torino rolling bearing test rig: description of the open-access data for vibration monitoring and diagnostics
<p>Accelerometric measurements from the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), Department of Mechanical and Aerospace Engineering, Politecnico di Torino.</p> <p>Goals:</p> <p> • Vibration Monitoring, Bearing Diagnostics, Damage detection, Damage localization, Damage classification, Damage assessment.</p> <p>Features:</p> <p> • high-speed spindle driving a hollow shaft supported by a couple of identical roller bearings B1 and B3. B1 is the bearing under analysis and features various damages.</p> <p> • two damage types (indentations on a roller and on the inner ring) and severities (0, 150, 250, 450 µm).</p> <p> • a central, larger roller bearing (B2) is loaded through a sledge generating a controlled radial force measured by a load cell.</p> <p> • lubrication is obtained by oil injection into the hollow shaft.</p> <p> • a K-type thermocouple is used to monitor the temperature (manually recorded).</p> <p> • two triaxial accelerometers are mounted on the supports of bearings B1 and B2.</p> <p>Dataset:</p> <p> • stationary acquisitions at different speed & load combinations (speed: 0, 100, 200, 300, 400, 500 Hz; load: 0, 1000, 1400, 1800 N).</p> <p> • endurance acquisitions of the bearing featuring the 450µm roller indentation. Monitoring of the damage evolution for about 230 hours under the same speed and load condition.</p> <p> </p> <p>The extended description of the dataset can be found in the attached pdf "Description and analysis of open access data" or in:</p> <p>A.P. Daga, A. Fasana, S. Marchesiello, L. Garibaldi, The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data, Mechanical Systems and Signal Processing 120 (2019) 252–273. doi:10.1016/j.ymssp.2018.10.010.</p>
Vibration analysis metrics of a ball bearing during different operational states
<p>This labeled dataset is provided in the form of a CSV file and contains vibration analysis metrics (v-RMS, a-RMS, a-Peak, Temperature, Crest Factor) of a 6204 2RS ball bearing by the use of an IFM VVB001 vibration sensor and measurements were captured every two minutes by the sensor device.<br> An experimental assembly was set up comprised by a 0.75kW - 1450rpm Bonfiglioli BN80B4 FD motor with an i=80 reduction gear, a coupler and an axle with the ball bearing mounted on the latter for the purpose of conducting experiments based on three operational states of the bearing in regard to the level of grease applied.</p> <p>Attribute description as per the sensor’s manual and operation instructions:</p> <ul> <li>The <strong>v-RMS</strong> (effective value of the vibration velocity) measures the total load of a rotating machine. The most frequent types of overload (unbalance, alignment errors, etc.) are reflected in the v-RMS. An increased load can damage the machine in the long term (fatigue, fatigue strength) or, in extreme cases, destroy it within a short time. It is expressed in m/s.</li> <li>The <strong>a-RMS </strong>(effective value of the acceleration) detects mechanical contact of machine components. This contact typically occurs in case of wear (faulty bearing, worn out toothed wheels, etc.) or problems with lubricants (contaminated grease, water in oil, etc.). It is expressed in m/s<sup>2</sup>.</li> <li>The <strong>a-Peak</strong> monitors the maximum value of the acceleration. Shocks in the acceleration can occur once or periodically, as in a crash, for example in the event of bearing damage. a-Peak is a measure for the forces occurring on the machine. It is expressed in m/s<sup>2</sup>.</li> <li>The <strong>Crest </strong>(or crest factor) is a described characteristic value of the signal analysis. It is defined as the ratio of the maximum value to the effective value (peak/RMS). In condition monitoring the characteristic value is used for the evaluation of the bearing condition. The high-frequency signals with a short pulse duration of a bearing damage generate higher peak values in relation to the effective value. This relation can be read from the crest factor.</li> <li>The <strong>Temperature </strong>attribute is self explanatory and is expressed in degrees of Celsius.</li> <li>Labels of the <strong>Bearing State</strong> regarding the recorded measurements:<br> 1 - sealed ball bearing with recommended amount of industrial grease<br> 2 - unsealed ball bearing with no amount of grease<br> 3 - unsealed ball bearing with excess amount of grease</li> </ul> <p>This dataset was produced for the purpose of training an Artificial Neural Network model for the task of multi-class classification in the frames of a Predictive Maintenance strategy.</p>
Effect Of Whole Body Vibration Versus Weight Bearing Exercises On Osteoporosis In Breast Cancer Patients After Chemotherapy
ClinicalTrials.gov study NCT05970250. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Whole-body-vibration training positively affects muscle transcriptome in tumour bearing cachectic mice
GEO Series GSE121972. Mus musculus. 58 samples. Type: Expression profiling by array.
Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions
<p><strong>Description:</strong></p> <p>This dataset has been created by the ISED group of Politecnico di Torino to address the lack of data available for developing fault detection models and/or predictive maintenance systems for medium/large-sized spherical roller bearings, commonly used in industrial settings.</p> <p>The data were collected using <a href="https://www.skf.com/it/products/rolling-bearings/roller-bearings/spherical-roller-bearings/productid-22240%20CCK%2FW33">SKF 22240 CCK/W33 spherical roller bearings</a> through an extensive experimental campaign on the medium/large-scale bearing test rig (capable of testing bearings with an outer diameter up to 420 mm), developed by the ISED research group at Politecnico di Torino (<a href="https://www.dimeas.polito.it/la_ricerca/gruppi/ised_industrial_systems_engineering_and_design">ISED Research Group</a>). The technical details of the test rig can be found in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>.</p> <p>The dataset contains data for individual localized defects applied to one of the four bearings tested simultaneously on the rig, which is capable of independently applying both axial and radial loads.</p> <p><strong>Dataset Structure:</strong></p> <p>The dataset is organized into four folders:</p> <ul> <li><code>Undamaged</code></li> <li><code>InnerRaceDamage</code></li> <li><code>OuterRaceDamage</code></li> <li><code>RollerDamage</code></li> </ul> <p>The <code>Undamaged</code> folder contains data for all bearings in healthy condition. The other folders contain data from tests where one of the bearings presents a localized defect. The defects, introduced by chip removal, have a diameter of 2 mm and a depth of 0.5 mm, affecting either the <strong>inner race</strong> (IR), <strong>outer race</strong> (OR), or <strong>roller elements</strong> (B). More detailed information about the defect geometry and location can be found in the following publications:</p> <ul> <li><a href="https://www.mdpi.com/1424-8220/23/1/211">Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/4/2038">Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/22/12458">Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks</a></li> </ul> <p>Each folder contains <code>.mat</code> files named according to the following format:</p> <p><code>(<em>Nominal_Rotation_Speed</em>)rpm_(<em>Radial_Force</em>)kN_(<em>Axial_Force</em>)kN.mat</code></p> <p>Where:</p> <ul> <li><strong>Nominal_Rotation_Speed</strong> is the machine's nominal rotational speed</li> <li><strong>Radial_Force</strong> is the radial force applied to the bearing</li> <li><strong>Axial_Force</strong> is the axial force applied to the bearing</li> </ul> <p>The dataset includes measurements from 10 different nominal rotation speeds and four load conditions, one of which includes an axial load. In some cases, "ramp" files are included, containing data where the rotational speed was linearly varied during the test.</p> <p>Each <code>.mat</code> file contains multiple structures, depending on the type of test. Each structure is labeled as <code>Signal_</code> followed by a number (0, 1, 2, 3, 4), where each represents a specific signal extracted during the test. There is no fixed correspondence between the signal number and the type of measurement. For example, <code>Signal_2</code> does not always represent the accelerometer signal. Users are encouraged to inspect the <code>y_values.quantity</code> field to identify the signal's unit and nature. For instance, if <code>y_values.quantity.label</code> shows "g", the signal corresponds to an accelerometric measurement.</p> <p>All signals have been exported using the <strong>MKS system</strong>, so <code>y_values.values</code> contains data in units of m/s² for acceleration signals. To convert the values to the unit indicated in <code>y_values.quantity.label</code>, users can apply the multiplication factor and offset provided in <code>y_values.quantity.unit_transformation</code>. In the case of accelerometric data, the multiplication factor is 0.1020, converting <code>y_values.values</code> from m/s² to g.</p> <p>A more detailed description of the data structure can be found in the Test.Lab documentation.</p> <p>In addition to acceleration data, the files include temperature signals, tacho sensor signals measuring shaft speed, and tacho impulse signals. In some cases, there is also a signal measured in "N", representing the frictional force generated by the bearings (as detailed in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>). This friction force signal is not always present, as the load cell could go into overload during certain tests.</p> <p><strong>Sensor Data Organization:</strong></p> <p>Acceleration and temperature data are presented in tables with four columns, each corresponding to one of the four bearings. In the damaged condition tests, the damaged bearing is always the one corresponding to sensor 4 (i.e., <code>y_values.values(:, 4)</code>).</p>
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