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5 results for “Vibration Sensor”
Vibration-based smart sensor for high flow dust measurement
<p><strong>Abstract:</strong> Drying process of aggregates needed for asphalt manufacturing involves a high quantity of dust or filler that needs to be heated and extracted with the aid of a baghouse. A sensor that is able to measure the amount of filler aspirated will be a relevant innovation as the current state of the art for drying of aggregates involves a high amount of energy to heat all the aggregates so the highest amount of dust or filler is extracted. The final step of asphalt production is to mix all the components like bitumen, aggregates and cold filler itself. In the context of European project CAPRI [1,2], it is presented a prototype for measurement of filler flow based on vibration analysis, inside the pipe with an accelerometer in the insulator of an existing thermocouple subjected to the hard conditions of temperature and pressure. The paper shows the laboratory prototype results together with preliminary onsite evaluation previously to final demonstration. The paper provides also open access to all the data and results used as part of the commitment of CAPRI project with open science.</p> <p><strong>Keywords:</strong> Sensors, Innovation, Process Industry, Automation, Industry 4.0, Digital Transformation, Industrial Plants, Filler, Dust, Vibration, Signal processing, Smart sensing.</p>
Vibration Sensor and Process Data from Ferrosilicon Production
<p><strong>Elkem Facility</strong></p> <p>The facility specializes in producing ferrosilicon (FeSi) and ferrosilicon magnesium (FSM) master alloys. Elkem Bjølvefossen is among the world’s largest producers of FSM. Three reduction furnaces deliver the base metal which is then alloyed and refined to the right quality of FeSi or FSM. These alloys are important additives in the manufacturing of steel products. Silicon in the form of FeSi is used to remove oxygen from the steel and as an alloying element to improve the final quality of the steel. Silicon increases strength and wear resistance, elasticity, i.e., spring steels, scale resistance, and heat resistant steels and lowers electrical conductivity and magnetostriction.</p> <p>After tapping and refining, the ferro-alloys are crushed to grains ranging from 1 mm to 25 mm in size. Consumers of FeSi and FSM have strict requirements for particle size, related mainly to the chemical kinetics of their refining and alloying processes. For this reason, the crushed material is separated in sieves and packaged by particle size before shipment. Two lattice gratings inside the Mogensen shaker separate the material according to required particle size. </p> <p>The subject of the present study is a mechanical shaker platform containing one or more such sieves. The shaker is a <a href="https://www.mogensen.se/">Mogensen S0556</a> that was installed in 1996 in Bjølvefossen and is no longer produced in this type. This device is powered by two counter-rotating 1.2-horsepower AC motors operating at 960 RPM. Together with the spring suspension, these cause an elliptical motion that both transports and scatters the incoming material across the sieve. The shaker is engineered so that the motion transitions from a slanted ellipse at the in-feed to nearly linear at the output.</p> <p><strong>Vibration Data</strong></p> <p>Vibration data from two sensors. Each sensor measures acceleration in three axes with three different ADCs. </p> <p><strong>ERP and MES data</strong></p> <p>Manufacturing Execution System (MES) data as well as process data from the Enterprise Resource Planning (ERP) is given. It is providing information about the material that is currently being produced as well as the data from the scales from the material packing station where the bags with completed production were packed. MES data has a resolution of 5 seconds and the process data from ERP has a resolution of roughly 10 minutes. This operational data is meant to provide insight into the current state and throughput of the facility and will serve as labels for the correlation analysis with the vibration data. </p>
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic pump
<p>The robotic pump demonstrator represents a hydraulic pump that can be mounted on an industrial robot, for example, to pump liquid paint for spray painting. On this pump, one accelerometer is mounted for vibration monitoring and recording.</p> <p>The pump can be controlled in terms of speed (rotations per minute, rpm), affecting the throughput of paint and the pressure in and out of the pump.</p> <p>The dataset consists of 380 million measurements of several sensor data of the pump system in 1-second intervals over two months in 2020. The data is split by the recording date over 33 files.</p>
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic wrist
<p>The robotic wrist demonstrator represents a mechanical wrist with three axes that can hold tools, e.g. for spray painting in combination with a pump. On this robotic wrist, two accelerometers are mounted for vibration monitoring and recording: one in the movable front part of the wrist and one in the shaft. The wrist can be controlled through the torque or the designated position of each axis’ motor.</p> <p>The dataset consists of 1.8 billion measurements of several sensor data of the robotic wrist in 1-second intervals over six months in 2020. The data is split by the recording date over 98 files.</p>
A dataset of throat-related events collected with a soft skin-attachable vibration sensor
<p>This dataset is collected using a skin-attachable sensor placed on the laryngeal prominence. The sensor measures various throat-related events, including coughing, speaking, swallowing, and throat clearing. The dataset is divided into a training set and a test set, each containing different types and quantities of events.</p> <p> </p> <p><strong>Training set</strong></p> <p>The training set comprises the following throat-related events:</p> <p>1. Coughing: 1,984 instances<br>2. Speaking (English): 2,107 instances<br>3. Swallowing: 2,028 instances<br>4. Throat Clearing: 2,089 instances</p> <p> </p> <p><strong>Test set</strong></p> <p>The test set includes a diverse range of events to evaluate the model's performance:</p> <p>1. Coughing: 150 instances<br>2. Speaking (multiple languages): 333 instances (comprising English, French, German, Korean, and Spanish)<br>3. Swallowing: 161 instances<br>4. Throat Clearing: 148 instances</p> <p> </p> <p>This dataset offers labeled throat-related events for training and testing machine learning models.</p>
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