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576 results for “monitoring system”
Condition monitoring of hydraulic systems Data Set at ZeMA
<p><strong>Abstract:</strong></p> <p>The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.</p> <p> </p> <p><strong>Source:</strong></p> <p>Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken<br> Contact: t.schneider <strong>'@'</strong> zema.de, s.klein <strong>'@'</strong> zema.de, m.bastuck <strong>'@'</strong> lmt.uni-saarland.de, info <strong>'@'</strong> lmt.uni-saarland.de</p> <p> </p> <p><strong>Data Set Information:</strong></p> <p>The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.</p> <p> </p> <p><strong>Attribute Information:</strong></p> <p>The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:<br> Sensor Physical quantity Unit Sampling rate<br> PS1 Pressure bar 100 Hz<br> PS2 Pressure bar 100 Hz<br> PS3 Pressure bar 100 Hz<br> PS4 Pressure bar 100 Hz<br> PS5 Pressure bar 100 Hz<br> PS6 Pressure bar 100 Hz<br> EPS1 Motor power W 100 Hz<br> FS1 Volume flow l/min 10 Hz<br> FS2 Volume flow l/min 10 Hz<br> TS1 Temperature °C 1 Hz<br> TS2 Temperature °C 1 Hz<br> TS3 Temperature °C 1 Hz<br> TS4 Temperature °C 1 Hz<br> VS1 Vibration mm/s 1 Hz<br> CE Cooling efficiency (virtual) % 1 Hz<br> CP Cooling power (virtual) kW 1 Hz<br> SE Efficiency factor % 1 Hz<br> <br> The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are<br> <br> 1: Cooler condition / %:<br> 3: close to total failure<br> 20: reduced effifiency<br> 100: full efficiency<br> <br> 2: Valve condition / %:<br> 100: optimal switching behavior<br> 90: small lag<br> 80: severe lag<br> 73: close to total failure<br> <br> 3: Internal pump leakage:<br> 0: no leakage<br> 1: weak leakage<br> 2: severe leakage<br> <br> 4: Hydraulic accumulator / bar:<br> 130: optimal pressure<br> 115: slightly reduced pressure<br> 100: severely reduced pressure<br> 90: close to total failure<br> <br> 5: stable flag:<br> 0: conditions were stable<br> 1: static conditions might not have been reached yet</p> <p> </p> <p><strong>Relevant Papers:</strong></p> <p>[1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.<br> [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1.<br> [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.</p> <p><br> </p> <p><strong>Citation Request:</strong></p> <p>Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.</p>
Development of a Self-Powered Structural Health Monitoring System for Transportation Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17PTAM03. Abstract of the final report is stated below for reference:</p> <p>"Roadways and bridges play an important role in the economic and social health of society by connecting commerce and people. Economic growth and population expansion pose considerable burden on the aging infrastructure (i.e., pavements and bridges). There is a pressing need to develop structural health monitoring (SHM) technologies capable of collecting infrastructure utilization data. Doing so inexpensively with self-powered systems will revolutionize infrastructure monitoring technology, and will improve decision making enabling roadway and bridge preservation. In this study, a self-powered battery-less structural health monitoring (SHM) system was developed. It is powered by a thermal energy harvester equipped with thermoelectric generators (TEGs) driven by temperature differentials between the top of asphalt pavements and their lower layers. An innovative 2-tier TEG harvester was designed to limit the downtime of the SHM system when the temperature differentials are insufficient to power a single unit. The 2-tier system requires a minimum of 2.1⁰C in temperature differential to generate the minimum of 40 mV needed to power the SHM system. The SHM system consists of a DC-DC booster to increase the voltage generated by the harvester, a buck controller to bring this voltage down to the 3.3 Volts required for powering the microcontroller, a microcontroller and a wireless transceiver for transmitting the data. Another transceiver carried on-board a pilot vehicle is needed to retrieve the data. Software was developed in this project to allow data communication between the two transceivers. The SHM system developed accepts analogue voltage input from any sensor that generates analogue voltage, (e.g., piezoelectric axle load sensors, strain gauges, temperature gauges and so on). A prototype of this SHM system was constructed and tested in the lab and it is ready for field implementation."</p>
AI-based real-time animal management system for flock monitoring in the husbandry of fattening turkeys
<p>The dataset was collected in a commercial turkey barn using a self-developed AI-based real-time animal management system to assess the behavior of turkeys. The data covers turkeys from the 6th to the 18th week of life.</p>
PUDL Raw EPA Hourly Continuous Emission Monitoring System (CEMS)
<p>US EPA hourly Continuous Emissions Monitoring System (CEMS) data. Hourly CO2, SO2, NOx emissions and gross load. Archived from <a href="https://campd.epa.gov/">https://campd.epa.gov/</a></p><p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources:</p><ul><li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li><li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li><li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li></ul><p>In order to enable more regular CEMS updates, this dataset replaces a prior PUDL EPA CEMS archive that pulled one annual file for each state. That archive is no longer updated but can be found <a href="https://zenodo.org/doi/10.5281/zenodo.4127054">here</a>.</p>
Data for: Spatial monitoring of flying insects over a Swedish lake using a CW lidar system
<p>Data for a field experiment on remote sensing of flying insects are supplied. A bistatic CW lidar system of the Scheimpflug type was employed, and echoes from flying insects over and close to a Swedish lake were recorded as read-out files from an array detector making observations along the emitted laser beam. The activated pixels of the detector could be converted into range data by triangulation. With a high detector read-out frequency, data on numerous insects could be obtained. Special emphasis was put on distinguishing between large and small insects (from the intensities of the recorded echoes) and the insect positions with regard to the shores of the lake. </p>
Design Of A Condition Monitoring System (CMS) For A Gas Compressor Using Shell Bonny Terminal As A Case Study
<p><strong>Subcontracted</strong> <strong>Researcher</strong> <strong>&</strong> <strong>Field</strong> <strong>Electrical</strong> <strong>and</strong> <strong>Electronics</strong> <strong>Engineer</strong>, <em>Shell</em> <em>Bonny</em> <em>Terminal, Nigeria</em>— 2013</p> <p>I served as a Hooks Electric subcontracted researcher and field electrical and electronics engineer at Shell Bonny Terminal, spearheading a groundbreaking project to design and construct a state-of-the-art software condition monitoring system (CMS) for a gas compressor. In addition to my technical responsibilities, I also took charge of integrating this innovative design into the existing SCADA (Supervisory Control and Data Acquisition) infrastructure of the plant.</p> <p>Working collaboratively with a team of four talented engineers, I successfully managed and coordinated their efforts throughout the project. Together, we leveraged our collective expertise to develop and implement a fully functional software condition monitoring system. This collaborative approach ensured that our project benefitted from diverse perspectives and skill sets, leading to a comprehensive and robust solution.</p> <p>Utilizing my proficiency in LabVIEW, C#, native C, Java and ActiveX library, I led the development of a cutting-edge data acquisition (DAQ) device for CM system. This device seamlessly captured sensor readings from the live gas compressor in real-time, while the systemic algorithm I designed skillfully tracked false alarms using fallback sensors. By monitoring critical parameters such as ambient and self-temperature, axial speed, vibration, inflow pressure, and outflow pressure, we were able to enhance the system's reliability and performance.</p> <p>Furthermore, I effectively integrated the software condition monitoring system into the plant's existing SCADA infrastructure, ensuring seamless communication and compatibility with the overall plant monitoring and control system. This integration facilitated centralized data management and provided comprehensive insights into the gas compressor's operation, enabling proactive maintenance and efficient decision-making.</p> <p>By successfully managing the team and overseeing the integration process, I ensured that our project aligned seamlessly with the plant's existing infrastructure, resulting in an optimized and harmonized system.</p> <p>Through our collective efforts, we delivered a highly sophisticated and fully integrated software condition monitoring system that significantly improved the management and performance of the gas compressor at Shell Bonny Terminal.</p> <p>I have curated all the resource files that will enable anyone replicate our work. This is shared under the CC-by-4 license.</p> <p>The LabVIEW codes are documented in Work.zip file and all manuals and equipment specifications have been uploaded. I have created a test bench to help anyone quickly test the LabVIEW software in simulation mode. </p> <p>I have also provided a slide presentation that summarizes our work, including methodologies and results.</p>
Accuracy of the Dexcom G6 Continuous Glucose Monitoring System Following Cardiac Surgery
ClinicalTrials.gov study NCT04569240. IPD Sharing: NO. Countries: 1. Publications: 5.
Closed-Loop Fluid Administration System Using Hemodynamic Monitors
ClinicalTrials.gov study NCT02020863. IPD Sharing: UNDECIDED. Countries: 1. Publications: 15.
Abbott Sensor Based Glucose Monitoring System Paediatric Study
ClinicalTrials.gov study NCT02388815. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Usability Study to Assess the Reveal LINQ Insertable Cardiac Monitor System
ClinicalTrials.gov study NCT01965899. IPD Sharing: Not stated. Countries: 7. Publications: 2.
Remote Glucose Monitoring System in Hospitalized Patients With Diabetic Ketoacidosis (DKA)
ClinicalTrials.gov study NCT05439928. IPD Sharing: NO. Countries: 1. Publications: 11.
AggRegated System Of sensOrs and Multimedia Monitors: Technology for innovAtion and personalizaTion of rEhabilitation Care. (ROOMMATE)
ClinicalTrials.gov study NCT06728020. IPD Sharing: YES. Countries: 1. Publications: 1.
Study to Monitor the Occurrence of Viral Variants in Patients With Compromised Immune Systems Being Treated for COVID-19
ClinicalTrials.gov study NCT05305651. IPD Sharing: YES. Countries: 1. Publications: 1.
Performance of an Investigational Blood Glucose Monitoring System
ClinicalTrials.gov study NCT01824355. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Systematic Aerobics and Intelligent Monitoring System in Executive Skill Training for School-aged Children With ADHD
ClinicalTrials.gov study NCT05668195. IPD Sharing: NO. Countries: 1. Publications: 6.
Nutrition Monitoring and Feeding Optimization With the smART+ System - Comparative Study
ClinicalTrials.gov study NCT04098224. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Perceived Efficiency and Acceptability of Remote Real-time Health Monitoring System in Elderly Residential Setting and Community Isolation Facility
ClinicalTrials.gov study NCT05302440. IPD Sharing: YES. Countries: 1. Publications: 1.
mobiCARE™ ECG Monitoring System
ClinicalTrials.gov study NCT06896734. IPD Sharing: NO. Countries: 1. Publications: 8.
Continuous Glucose Monitoring System (CGM)-Informed Bolus Calculator Study
ClinicalTrials.gov study NCT04320069. IPD Sharing: NO. Countries: 1. Publications: 1.
interACTION: A Portable Joint Function Monitoring and Training System for Remote Rehabilitation Following TKA
ClinicalTrials.gov study NCT02646761. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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