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60 results for “Electromechanics”
Sensor data set, electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in HDF5 file as a 3D-matrix</li> <li>one row represents one second of the return stroke of one working cycle (6292 rows: 6292 cycles)</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
Sensor data set of 3 electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in three HDF5 file as a 3D-matrix, one file is for one axis</li> <li>one row represents one second of the return stroke of one working cycle<br> axis 3: 6292 cycles<br> axis 5: 6083 cycles<br> axis 7: 5732 cycles</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
Data from: A perspective on hybrid quantum opto- and electromechanical systems
<p>Source data for Figure 2.</p>
Dataset_electromechanical model for electro-ribbon actuator
<p>Experimental data</p>
Data for the article: "A quantum electromechanical interface for long-lived phonons"
<p>The data used for generating the figures in the article: "A quantum electromechanical interface for long-lived phonons".</p>
Electromechanical Profiling of the Long-QT Syndrome (LQTS)
ClinicalTrials.gov study NCT04074122. IPD Sharing: UNDECIDED. Countries: 0. Publications: 6.
Laparoscopic Enclosed Morcellation; Electromechanic Morcellation vs Vaginal Removal
ClinicalTrials.gov study NCT02737553. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Hydrothermally synthesized PZT film grown in highly concentrated KOH solution with large electromechanical coupling coefficient for resonator
Open the record for dataset details and reuse information.
Measurement of the Electromechanical Window to Improve the Diagnosis of Congenital Long QT Syndrome
ClinicalTrials.gov study NCT04328376. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Ablation Guided Via Precision Imaging Using Electromechanical Wave Imaging
ClinicalTrials.gov study NCT06577714. IPD Sharing: NO. Countries: 1. Publications: 0.
Reduction or Extension of COnduction Time With Ventricular Electromechanical Remodeling (RECOVER)
ClinicalTrials.gov study NCT04397224. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Atrial Electromechanical Interval and Pulse Wave Velocity in the Prediction of Recurrence of Atrial Fibrillation
ClinicalTrials.gov study NCT00818012. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Characterisation Data - Electromechanical Characterization
<p>st_1</p>
Characterisation Data - Electromechanical Characterization
<p>Bending_coupon_1</p>
Characterisation Data - Electromechanical Characterization
<p>pristine_3_fatigue_dat</p>
Electromechanical Characterization
<p>SmartFan Project public data</p>
Prognostic Health-Management System Development for Electromechanical Actuators
Electro-mechanical actuators (EMAs) have been gaining increased acceptance as safety-critical actuation devices in the next generation of aircraft and spacecraft. The aerospace manufacturers are not ready, however, to completely embrace EMAs for all applications due to apprehension with regard to some of the more critical fault modes. This work aims to help address these concerns by developing and testing a prognostic health management system that diagnoses EMA faults and employs prognostic algorithms to track fault progression and predict the actuator remaining useful life. The diagnostic algorithm is implemented using a combined model-based and data-driven reasoner. The prognostic algorithm, implemented using Gaussian Process Regression, estimates the remaining life of the faulted component. The paper also covers the selection of fault modes for coverage and methods developed for fault injection. Validation experiments were conducted both in laboratory and flight conditions using the Flyable Electromechanical Actuator (FLEA) test stand. The FLEA allows test actuators to be subjected to realistic environmental and operating conditions, while providing the capability to safely inject and monitor propagation of various fault modes. The paper covers both diagnostic and prognostic, run-to-failure experiments, conducted in laboratory and flight conditions for several types of faults. The experiments demonstrated robust fault diagnosis on the selected set of component and sensor faults and high-accuracy predictions of failure time in prognostic scenarios.
Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators
Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.
Data set from Pappone C, Mecarocci V, Manguso F, Ciconte G, Vicedomini G, Sturla F, Votta E, Mazza B, Pozzi P, Borrelli V, Anastasia L, Micaglio E, Locati E, Monasky MM, Lombardi M, Calovic Z, Santinelli V. New electromechanical substrate abnormalities in high-risk patients with Brugada syndrome. Heart Rhythm. 2020 Apr;17(4):637-645. doi: 10.1016/j.hrthm.2019.11.019. Epub 2019 Nov 19. PMID: 31756528.
<p>Data set from Pappone C, Mecarocci V, Manguso F, Ciconte G, Vicedomini G, Sturla F, Votta E, Mazza B, Pozzi P, Borrelli V, Anastasia L, Micaglio E, Locati E, Monasky MM, Lombardi M, Calovic Z, Santinelli V. New electromechanical substrate abnormalities in high-risk patients with Brugada syndrome. Heart Rhythm. 2020 Apr;17(4):637-645. doi: 10.1016/j.hrthm.2019.11.019. Epub 2019 Nov 19. PMID: 31756528.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong> The relationship between the typical electrocardiographic pattern and electromechanical abnormalities has never been systematically explored in Brugada syndrome (BrS).</p> <p><strong>Objectives: </strong> The aims of this study were to characterize the electromechanical substrate in patients with BrS and to evaluate the relationship between electrical and mechanical abnormalities.</p> <p><strong>Methods: </strong> We enrolled 50 consecutive high-risk patients with BrS (mean age 42 ± 7.2 years), with implantable cardioverter-defibrillator implantation for primary or secondary prevention of ventricular tachyarrhythmias (ventricular tachycardia/ventricular fibrillation [VT/VF]), undergoing substrate mapping and ablation. Patients underwent 3-dimensional (3D) echocardiography with 3D wall motion/deformation quantification and electroanatomic mapping before and after ajmaline administration (1 mg/kg in 5 minutes); 3D mechanical changes were compared with 50 age- and sex-matched controls. The effect of substrate ablation on electromechanical abnormalities was also assessed.</p> <p><strong>Results: </strong> In all patients, ajmaline administration induced Brugada type 1 pattern, with a significant increase in the electrical substrate (P < .001), particularly in patients with previous spontaneous VT/VF (P = .007). Induction of Brugada pattern was associated with lowering of right ventricular (RV) ejection fraction (P < .001) and worsening of 3D RV mechanical function (P < .001), particularly in the anterior free wall of the RV outflow tract, without changes in controls. RV electrical and mechanical abnormalities were highly correlated (r = 0.728, P < .001). By multivariate analysis, only the area of RV dysfunction was an independent predictor of spontaneous VT/VF (odds ratio 1.480; 95% confidence interval 1.159-1.889; P = .002). Substrate ablation abolished both BrS-electrocardiographic pattern and mechanical abnormalities, despite ajmaline rechallenge.</p> <p><strong>Conclusion: </strong> BrS is an electromechanical disease affecting the RV. The typical BrS pattern reflects an extensive RV arrhythmic substrate, driving consistent RV mechanical abnormalities. Substrate ablation abolished both Brugada pattern and mechanical abnormalities.</p> <p> </p>
Single photon induced instabilities in a cavity electromechanical device
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