The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
The bench of experiments is on the premises of the School of Engineeringof São Carlos (EESC) of the University of São Paulo (USP), Brazil, more specifically in theLaboratory of Intelligent Automation of Processes and Systems (LAIPS) and Laboratory ofIntelligent Control of Electrical Machines (LACIME).
The three-phase induction motor is a model of the W22 standard line from manufacturer WEG, 1 cv, 220V / 380V, 3.02A / 1.75A, 4 poles, 60 Hz, with a nominal torque of 4.1 N.m and nominal speed of 1715 rpm. The rotor is a squirrel cage type made up of 34 bars. It is driven by means of a control panel that allows the selection of the type of drive, star or triangle, and the type of supply, direct mains voltage or via a three-phase inverter.
The rotary torque wrench used in the research is the Transtec model MT-103, with a maximum rotation of 2000 rpm, based on Wheatstone bridge technology and with a sensitivity of 2 mV / V. Its main function is to allow visualization of the torque present in the shaft, which will be varied simulating various operating conditions of the induction motor.
Manual adjustment of the resistant torque is done by varying the field winding voltage of the direct current generator. Therefore, to reduce the magnitude of the grid voltage, a 1800W single-phase voltage variation is used by Variac, and to convert the alternating voltage to continuous, a single-phase rectifier is used which feeds the field winding.
The vibration sensors used were Vibrocontrol uniaxial accelerometers, model PU 2001, with sensitivity of 10 mV / mm / s, frequency range 5 to 2000 Hz and stainless-steel housing, which provides the integrated acceleration signal over time. , ie provides the measure of vibration velocity. In total five accelerometers were used simultaneously, located non-drive end side motor, drive end side motor, housing, in the axial direction of the motor, and on the support desk. Therefore, these monitoring points allow the measurement of axial, tangential and radial velocity.
The currents were measured using alternating current probes, which correspond to precision meters, with a capacity of up to 50 A RMS, with an output voltage of 10 mV / A, corresponding to the Yokogawa model 96033. The voltages were measured directly at the MIT terminals using oscilloscope voltage tips also from the manufacturer Yokogawa.
To simulate the failure of broken bars in the squirrel cage rotor of the three-phase induction motor it was necessary to drill the rotor. Drilling was carried out by means of a bench drill mounted with a 6 mm diameter drill to ensure that the diameter of the hole exceeds the width of a rotor bar, with the tip centered at half the longitudinal length of the rotor.
Since in a real situation the breaking rotor bars are usually adjacent to the first broken bar, 4 rotors were tested, the first with one broken bar, the second with two adjacent broken bars, and so on to the rotor containing four adjacent bars. broken . It is worth mentioning that the hub depth of all tested rotors was the same, corresponding to 20 mm.
Thus, a rotor without a hole was tested first, that is, a healthy rotor, and then it was successively replaced in order to obtain a database of monitored variables.
Experiments were carried out using the bench mentioned above for the construction of the database. Tests were carried out on healthy motors and motors with defects in direct start with balanced three-phase supply voltage and 60 Hz frequency.
For the preparation of a reliable database, enabling future work were applied 0.5nm shipments, 1,0Nm, 1,5Nm, 2,0Nm, 2,5Nm, 3,0Nm, 3,5Nm, and 4.0Nm to the axis of the three-phase induction motor. For each loading condition of the motor shaft, ten repetitions were performed.
In this way, using the data acquisition system, for each experiment of each loading, the following variables were acquired:
· voltages in phases A, B, and C;
· currents in phases A, B, and C;
· mechanical vibration speeds tangential in the housing, tangential in the base, axial on the driven side, radial on the driven side, and radial on the non-drive side.
This experimental process was performed for the detection and diagnosis of failures for healthy engines and engines with rotors containing 1, 2, 3, and 4 bars broken adjacent.
The database is organized as a structure of the Matlab application. The “struct_rs_R1” structure presents the experimental data referring to the defectless induction motor, “struct_r1b_R1” referring to the rotor with one broken bar, “struct_r2b_R1” referring to the rotor with two broken bars, “struct_r3b_R1” referring to the rotor with three broken bars and “Struct_r4b_R1” for the rotor with four broken bars.
When loading the files containing the experimental data for each structure in the Matlab application, it will be possible to view the experimental data for each of the mechanical loads imposed on the motor shaft. Then, it will be possible to observe the experimental data for each monitored variable.
This dataset was extracted from Twitter using keywords related to Dilma Roussef and Aécio Neves, that were the candidates of the second round of the 2014 presidential election in Brazil. This dataset contains texts in Portuguese and the respective classification of sentiments resulting from the techniques described in the article published in the 2018 IEEE International Conference on Data Mining Workshops - ICDMW (https://ieeexplore.ieee.org/abstract/document/8637504).
The .zip file is divided into four .csv files with data organized in 11 columns named: date, amount of retweets, amount of favorites, tweet text, mentions, hashtags, id, permalink, a score of classification, label of sentiment.
The VND MO test benchmark problems
We build an original dataset of thermal videos and images that simulate illegal movements around the border and in protected areas and are designed for training machines and deep learning models. The videos are recorded in areas around the forest, at night, in different weather conditions – in the clear weather, in the rain, and in the fog, and with people in different body positions (upright, hunched) and movement speeds (regu- lar walking, running) at different ranges from the camera.
About 20 minutes of recorded material from the clear weather scenario, 13 minutes from the fog scenario, and about 15 minutes from rainy weather were processed. The longer videos were cut into sequences and from these sequences individual frames were extracted, resulting in 11,900 images for the clear weather, 4,905 images for the fog, and 7,030 images for the rainy weather scenarios.
A total of 6,111 frames were manual annotated so that could be used to train the supervised model for person detection. When selecting the frames, it was taken into account that the selected frames include different weather conditions so that in the set there were 2,663 frames shot in clear weather conditions, 1,135 frames of fog, and 2,313 frames of rain.
The annotations were made using the open-source Yolo BBox Annotation Tool that can simultaneously store annotations in the three most popular machine learning annotation formats YOLO, VOC, and MS COCO so all three annotation formats are available. The image annotation consists of a centroid position of the bounding box around each object of interest, size of the bounding box in terms of width and height, and corresponding class label (Human or Dog).
Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3 and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity.
This dataset contains the results of the simulation runs of the experiments performed to evaluate and compare the proposed spatial model for situated multi-agent systems. The model was introduced in a paper entitled "BioMASS, a spatial model for situated multiagent systems that optimizes neighborhood search". In this paper we presented a new model to implement a spatially explicit environment that supports constant-time sensory (neighborhood search) and locomotion functions for situated multiagent systems.
The dataset include a compressed file in zip format. It contains a directory structure as shown below. Each directory is a specific experiment with each simulation toolkit and parameters. Inside each directory there are 50 CSV files, one for echa simulation run. Each file has a header describing the main parameters of the corresponding experiment. We use the Repast Toolkit, and Mason Toolkit to perform a benchmark with the proposed BioMASS spatial model.
A Indústria enfrenta desafios graves e fracassa sem competitividade. Atacando esta problemática, conferiu-se o oferecimento de maior eficiência a processos industriais para promover a produtividade, elevar a qualidade e impulsionar mudanças. A solução desenvolvida incluiu dispositivos com sensores não invasivos, simples de instalar, que contabilizam os itens sendo transportados em linhas de produção.
Os dados foram coletados utilizando o dispositivo IoT da EnergyNow Tecnologias denominado Prodbox™, o qual opera como um equipamento empregado para intensificar a produtividade e apontar maneiras estratégicas de modificar variáveis que interferem na visão de gestão sobre a produção.
O dispositivo utiliza sensores não obstrutivos para contabilizar o número de itens que atravessam a linha de detecção gerada entre o transmissor e o receptor instalados.
Notadamente, os dados coletados são enviados para a nuvem, onde podem, quando integrados a uma plataforma de análise, ser processados para apresentar indicadores de acompanhamento de produtividade. Um sistema inteligente pode processar os dados coletados e apresentar métricas que permitem ao gestor identificar formas de aumentar a produção, bem como etapas que estão prejudicando a produtividade. Além disso, alertas customizados podem ser configurados para prover informação sobre a parada ou inatividade detectada pelo dispositivo.
Os dados gerados através do dispositivo podem ser utilizados para entender melhor variáveis sobre o ritmo de produção e, a partir delas, fomentar projeções de produção, calculando-se a relação entre itens produzidos e período de tempo necessário (segundos, minutos, horas, dias, semanas, etc).
Algumas sugestões sobre abordagens a serem consideradas:
Verifique se políticas de aumento de produtividade estão sendo efetivas.
Distribuia melhor os funcionários em etapas diferentes de uma linha de produção.
Correlacione etapas de produção com variáveis que estejam interferindo na produtividade para resolver problemáticas internas.
Demonstrating dataset used in one of the experiments.
Building Character Graphs and Dividing Communities in Chinese Novels Based on Graph Data Extraction: Community Division for Character Emotional Polarity Networks
script file below
Each voice sample is stored as a .WAV file, which is then pre-processed for acoustic analysis using the specan function from the WarbleR R package. Specan measures 22 acoustic parameters on acoustic signals for which the start and end times are provided.
The output from the pre-processed WAV files were saved into a CSV file, containing 3168 rows and 21 columns (20 columns for each feature and one label column for the classification of male or female).