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562 results for “Faults”
Data from: Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN
Gearbox plays most essential role in the modern machinery for transmitting the required torque along with motion and contributes to wide range of applications. Any failure in gearbox components affects the productivity and efficiency of the system. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence leads to failure of the whole mechanism. Ensemble Empirical Mode Decomposition (EEMD) comprises advancement and valuable addition in Empirical Mode Decomposition (EMD) and has been widely used in fault detection of rotating machines. However, intrinsic mode functions (IMFs) produced by EEMD often carry the residual noise. Also, the produced IMFs are different in number due to addition of white Gaussian noise, which leads to final averaging problem. To alleviate these drawbacks, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was previously presented. This paper describes and presents the implementation of CEEMDAN for fault diagnosis of simulated local defects using sound signals in a fixed-axis gearbox. Statistical parameters are extracted from decomposed sound signals for different simulated faults. Results shows the effectiveness of CEEMDAN over EEMD in order to obtain more accurate IMFs and fault severity.
Vibration-Based Fault Detection in Drone using Artificial Intelligence
<p>Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most of the studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases where due to an accident, the arm<br> of the multirotor crack or loose. This is normally unnoticeable without disassembly and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash.</p> <p>Two types of AI methods are incorporated in this study, namely, fuzzy logic and neuro-fuzzy, using the fuzzy logic and ANFIS toolbox in the MATLAB software, respectively. For the neuro-fuzzy approach, we use 100 and 1000 datasets to determine the effects of the dataset size on the neuro-fuzzy performance. Both datasets are divided into training (80%), testing (10%), and checking data (10%). The guidelines for constructing the AI algorithms are based on the experimental data for five experimental conditions; (i) (a) Original multirotor condition without modifying the multirotor arms, (b) 100% screwed multirotor arms condition (full tighten), (c) 50% screwed multirotor<br> arms condition (half tighten), (d) 10% screwed multirotor arms condition, and (e) Unscrewed multirotor arm conditions.</p> <p>Their results are compared to determine the best method in predicting the safety of the multirotor. Both methods provided acceptable decision making but the neuro-fuzzy approach depends on the dataset used as overfit model might give incorrect decision making. Because the vibration data are collected in an indoor environment, this framework is more suitable for early prediction before flying the multirotor outdoor. A video demonstrating the real-time deployment of our proposed method is included in the mp4 format.</p>
Software Architecture Catalog for Fault-Tolerant Containerized Systems
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Fourier transform infrared spectroscopy spectra from North Anatolian fault peridotite xenoliths
<p>Fourier transform infrared spectroscopy spectra from North Anatolian fault peridotite xenoliths.</p>
Supplemental material for Towards Better Localization of Predicate-Related Faults in SBFL
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Quaternary-Host Faults Database 2.0 (Southern Italy)
<p>The “<strong>Q-host faults database 2.0</strong>” is an original compilation, on a GIS platform, of Quaternary faults hosting the Structural Sites (SS) of the QUIN 2.0 database and neighboring to them and follows the approach adopted by Lavecchia et al. (2022) in QUIN 1.0 (https://doi.org/10.5281/zenodo.6412501; https://doi.org/10.1038/s41597-022-01311-8).</p> <p>The Q-host faults database 2.0 is supplementary to "QUIN 2.0 - new release of the QUaternary fault strain INdicators database from the Southern Apennines of Italy", which is the second release of the QUIN 1.0 database. </p> <p>Authors of the related article: Giusy Lavecchia<sup>1,2</sup>, Simone Bello<sup>1,2<strong>*,</strong></sup>, Carlo Andrenacci<sup>1,2</sup>, Daniele Cirillo<sup>1,2</sup>, Federico Pietrolungo<sup>1,2</sup>, Donato Talone<sup>1,2</sup>, Federica Ferrarini<sup>1,2</sup>, Rita de Nardis<sup>1,2</sup>, Paolo Galli<sup>3,4</sup>, Joanna Faure Walker<sup>5</sup>, Claudia Sgambato<sup>6</sup>, Marco Menichetti<sup>2,7</sup>, Carmelo Monaco<sup>2,8,9</sup>, Salvatore Gambino<sup>2,8</sup>, Giorgio De Guidi<sup>2,8</sup>, Giovanni Barreca<sup>2,8</sup>, Francesco Carnemolla<sup>2,8</sup>, Fabio Brighenti<sup>2,8</sup>, Salvatore Giuffrida<sup>2,8</sup>, Filippo Carboni<sup>2,10,11</sup>, Luigi Ferranti<sup>2,12</sup>, Luisa Valoroso<sup>13</sup>, Giovanni Toscani<sup>2,14</sup>, Massimiliano R. Barchi<sup>2,11</sup>, Gerald Roberts<sup>6</sup> & Francesco Brozzetti<sup>1,2</sup></p> <p>*corresponding author: simone.bello@unich.it </p> <p>1. DiSPuTer, Università degli Studi “G. d’Annunzio” Chieti-Pescara, Chieti, Italy. 2. CRUST - Centro inteRUniversitario per l’analisi Sismotettonica Tridimensionale, Chieti, Italy. 3. Dipartimento della Protezione Civile, 00193 Rome, Italy. 4. Istituto di Geologia Ambientale e Geoingegneria (IGAG) del Consiglio Nazionale delle Ricerche (CNR), Monterotondo 00016 Rome, Italy. 5. Institute for Risk and Disaster Reduction, University College London, London, UK. 6. Department of Earth and Planetary Sciences, Birkbeck, University of London, London, UK. 7. Università degli Studi di Urbino Carlo Bo, Urbino, Italy. 8. Dipartimento di Scienze Biologiche Geologiche e Ambientali, Università di Catania, 95129, Italy. 9. Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo-Sezione di Catania, Catania, Italy. 10. Institute of Earth and Environmental Sciences (Geology), Albert-Ludwigs-University Freiburg, Germany. 11. Dipartimento Fisica e Geologia, Università Degli Studi di Perugia, Italy. 12. DiSTAR, Università degli Studi di Napoli Federico II, Naples, Italy. 13. Istituto Nazionale di Geofisica e Vulcanologia, Rome. 14. Dipartimento di Scienze della Terra e dell’Ambiente, Università di Pavia, Italy.</p>
Dataset for intelligent fault detection
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Dispersion curves and Vs models in Bomi section of Jiali fault, SE Tibet
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Supporting dataset -C for "Crystallization of FAPbI3: polytypes and stacking faults"
<p>XYZ trajectory files of (111)-3R seeded growth of FAPbI3</p>
Supplemental Material for Context Switch Sensitive Fault Localization
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Digital Appendix: Seismotectonics of the Rawil Depression (Western Alps, Switzerland): Revisiting Faults, Earthquakes, and Crustal Stresses [Dataset]
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Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</p>
Studying the Impact of Early Test Termination Due to Assertion Failure on Code Coverage and Spectrum-based Fault Localization
<p>A dataset for early test termination</p>
Raw data and analysis scripts underlying the publication "Grover's algorithm in a four-qubit silicon processor above the fault-tolerant threshold"
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Data from: Ambient noise tomography of upper crustal structures and Quaternary faults in the Seoul metropolitan area and its geological implications
<p class="0"><span><span><span>We investigate the upper-crustal seismic velocity structure in the Seoul metropolitan</span> <span>area, where about 20 million people live. The Chugaryeong fault zone (CFZ) is</span> <span>placed in this area, but the seismic hazard potential remains unclear. We conducted</span> <span>ambient noise tomography to illuminate the high-resolution upper-crustal structure</span> <span>in the Seoul metropolitan area. We analyzed continuous vertical seismic records</span> <span>for ~5 months from a dense seismic array with 77 broadband stations. Group</span> <span>velocity dispersion curves and tomographic maps were extracted between 0.5 and 10</span> <span>s periods. We inverted 3-D group velocity tomography models up to a depth of ~10</span> <span>km from the group velocity maps. The shear-wave velocity model is consistent with</span> <span>the geological features. High-velocity anomalies at shallow depths are correlated</span> <span>with the surface topography and geology. The CFZ is located at a low velocity below the 5 km depth and presented as the simplified model. The large </span><span>V</span><span><sub>S</sub> </span><span>contrast</span> <span>regions are located beneath NS-trending faults. The cross-sections coincide with</span> <span>the near-vertical strike-slip faults in this area. In the southern region of the Seoul</span> <span>metropolitan area, low-velocity anomalies correlate with high heat flow regions. Our</span> <span>results effectively suggest high resolution upper-crustal structures and subsurface</span> <span>hidden faults in the urban area.</span></span></span></p>
Rupture pattern exposed by trenches on western and eastern branches of the Xiaojiang Fault zone
<p>Datas online are directly used in the research. </p>
Competing controls of fluid overpressurization and chloritization on strength and instability of faults in granite: Implications for seismicity triggered by fluid-injection
<p>Fluids injection for hydraulic stimulation and fracturing, typical in the development of enhanced geothermal systems (EGS) in granites, can reactivate deep faults and induce seismicity. Such faults typically contain chlorite coatings as an alteration product that may impact styles of deformation – aseismic through seismic. We performed shear experiments on simulated granite fault gouges under conditions typifying a geothermal reservoir at ~4 km depth with a confining pressure of 110 MPa, a temperature of 150℃, fluid pressures of 21-80 MPa, and chlorite contents of 0-100%, to investigate the influence of effective stress and mineral composition on fault strength and stability. Our results show a transition from velocity-strengthening to velocity-weakening behavior in simulated granite gouge when the fluid pressure was elevated from 21 to 80 MPa, characterized by a transition from fault compaction to dilation – as revealed by microstructural observations – with implications in enabling unstable failure. Conversely, increasing chlorite content stabilizes slip but reduces frictional strength. The microstructures of these mixed gouges exhibit shear localized on chlorite-enriched planes and promoting fault sliding. These results suggest that earthquake ruptures occurring during fluid injection can be facilitated by fluid overpressures. And that controlling fluid overpressures and being aware of the presence of alteration minerals are both important controls in mitigating such injection-induced seismic risks.</p>
Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry [DATA]
<p>Data and metadata for the publication "Laboratory evidence supporting a mechanical model describing the dynamic formation of fault damage asymmetry", published in Geophysical Research Letters.</p>
data of 5GC network fault
<p>include alarm information,network element information and performance metrics</p> <ul> <li>01.performance metrics:Performance index data such as time and index value when the digital twin network element is running;</li> <li>02.alarm information:Alarm information occured by the digital twin network element, such as the occurrence time and the alarm name;</li> <li>03.network element information:Basic information of digital twin network elements such as name, generator room;</li> </ul>
EDS and XRF compositional maps of slickenlines from Plan de los Platanos (PP), Big Piute (BP), Waterman Hills (WH) Faults
<p>EDS and XRF compositional maps from PP-(AU), BP, and WH samples.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.