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.

Instructions: 

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.

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In order to test the performance of the proposed sensor, the measurement error of the original sensor is tested.In addition, error test, stability test and repeatability test are carried out for the optimized sensor model.

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For Lissajous scanning the synchronization of both axis is crucial. The laser beam is deflected vertically by the first MEMS mirror, redirected to the second mirror and deflected horizontally. In the proposed master slave concept, the synchronization controller Φ compensates for relative phase errors by duty cycle adjustments while individual PLLs keep each MEMS mirror stabilized. This videos show how the projected grid and center pixel drifts if the synchroniaztion controller between both MEMS mirrors with individual PLLs is turned off.

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This work contains data gathered by a series of sensors (PM 10, PM 2.5, temperature, relative humidity, and pressure) in the city of Turin in the north part of Italy (more precisely, at coordinates 45.041903N, 7.625850E). The data has been collected for a period of 5 months, from October 2018 to February 2019. The scope of the study was to address the calibration of low-cost particulate matter sensors and compare the readings against official measures provided by the Italian environmental agency (ARPA Piemonte).

Instructions: 

A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN

Documentation for the air pollution monitoring station developed at Politecnico di Torino by:
Edoardo Giusto, Mohammad Ghazi Vakili under the supervision of Prof. Bartolomeo Montrucchio.

System Overview

This section includes a description of our architecture from several points of view, going from the hardware and software architecture, to the communication protocols.

Hardware Architecture

We target the following key characteristics of our system:

  1. The rapid and easy prototyping capabilities,
  2. Flexibility in connection scenarios, and
  3. Cheapness but also dependability of components.

As each board has to include a limited number of modules, to facilitate our prototype development, we select the Raspberry Pi single-board computer as a monitoring board.
Due to our constraints in terms of cost, size and power consumption we select its Zero Wireless version based on the ARM11 microprocessor.

The basic operating principle of the system is the following. The data gathered from the sensors are stored in the MicroSD card of the RPi. At certain time intervals the RPi tries to connect to a Wi-Fi network and, if such a connection is established, it uploads the newly acquired data to a remote server.
The creation of the Wi-Fi network is achieved using a mobile phone set to operate as personal hot-spot, while on the remote server resides the database storing all the performed measurements.

Software Architecture

Wi-Fi connectivity was one of the requirements for the system, but at the same time, the system itself should have not to produce unnecessary electromagnetic noise, possibly impacting the operating ability of the host's appliances.
To reduce the time in which the Wi-Fi connection was active, the Linux OS was set to activate the specific interface at predefined time instants in order to connect to the portable hot-spot.
Once connected to the network, the system performed the following tasks:

  1. synchronization of the system and RTC clock with a remote Network Time Protocol (NTP) server,
  2. synchronization of the local samples directory with the remote directory residing on the server.
    The latter task is performed using the UNIX rsync utility, which has to be installed on both the machines.

To gather data from the sensors, a Python program has been implemented, which runs continuously with a separate process reading from each physical sensor plugged to the board and writing on the MicroSD card.
It has to be noted that for what concerns the PM sensors, since the UART communication had to take place using GPIOs, a Pigpiod deamon has been leveraged, to create digital serial ports over the Pi's pins.

The directories on the remote server are a simple copy of the MicroSD cards mounted on the boards.
Data in these directories have been inserted in a MySQL database.

Mechanical Design and Hardware Components

In order to easily stack more than one device together, a 3D printed modular case has been designed.
Several enclosing frames can be tied together using nuts and bolts, with the use of a single cap on top.
Figure shows the 3D board design, together with the final sensor and board configurations.

Each platform is equipped with 4 PM sensors (a good trade-off between size and redundancy), 1 Temperature (T) and Relative Humidity (HT) sensor and 1 Pressure (P) sensor.
As our target was to capture significant data sampling for the particulate matter we adopt the following sensors:

  1. The Honeywell HPMA115S0-XXX as PM sensor.
    As one of our targets was to evaluate these sensors' suitability for air pollution monitoring applications, we insert 4 instances of this sensor in every single platform.
    This sort of redundancy allows us to detect strange phenomena and to avoid several kind of malfunctions, making more stable the overall system.

  2. The DHT22 as temperature and relative humidity sensor.
    This is very widespread in prototyping applications, with several open-source implementation of its library, publicly available on the internet.

  3. The Bosch BME280 as a pressure sensor.
    This is a cheap but precise barometric pressure and temperature sensor which comes pre-soldered on a small PCB for easy prototyping.

The system also includes a Real Time Clock (RTC) module for the operating system to retrieve the correct time after a sudden power loss. The chosen device is the DS3231.
The DS3231 communicates via I2C interface and has native support in the Linux kernel.

As a last comment, notice that a Printed Circuit Board (PCB) was designed to facilitate connections and soldering of the various sensors and other components.

Database

Create database

The database structure can be created using the scripts located in the mysql_insertion folder of the Dataset/SQL_Table repository.

mysql -u <user> [-h <host>] [-p] < create_db.sql

Load SQL data (SQL Format)

Data formated in SQL can be loaded using the mysql command mysql -u username -p WEATHER_STATION < db_whole_data.sql, and the db_whole_data.sql is available in the SQL_data/ folder of the Dataset directory.

Load RAW data (CSV)

Data can be loaded using the python script sql_ins.py available in the mysql_insertion folder of the Dataset/SQL_Table repository.

python sql_ins.py <data_folder>

The script assumes the following folder structure:

* data_folder
|-- 01-board_table
|-- 02-unit_of_measure_table
|-- 03-param_type_table
|-- 04-board_config_table
|-- 05-physical_sensor_table
|-- 06-logical_sensor_table
|-- 07-board_sensor_connection_table
|-- 08-measure_table
|-- arpa
|-- mobility
|-- stations

Each folder contains a set of csv files. The script automatically loads data into the appropriate table and using the correct fields, which are specified as a list of parameters in the script. It is possible to edit the script to load only a subset of the folders.

System Usage

To replicate the experiments, the user should clone the raspberry pi image into a MicroSD (16-32 GB).
To do this, s/he can issue the command dd if=/path/to/image of=/path/of/microsd bs=4m on Linux.
The sampling scripts are run by a systemd unit automatically at system startup. The same systemd unit handles also the automatic respawn of the processes if some problems occur. The data are stored in the /home/alarm/ws/data directory, with filenames corresponding to the date of acquisition.

In order to upload these data to a database, it is possible to use the guide contained in the "database" directory.

In order to perform calibration and tests, it is recommended to take a look at the guide contained in the "analysis" directory. A Python class has been implemented to perform calibration of sensors against the ARPA reference ones. The resulting calibration can then be applied to a time window of choice.

3D Model

A 3D model of the case has been developed using SketchUp online software.
The resulting model is split in 5 different parts, each large enough to fit in our 3D printer (Makerbot Replicator 2X).
The model is stackable, meaning that several cases can be put on top of each other, with a single roof piece.

Printed Circuit Board

A PCB has been developed using KiCad software, so to create a hat for the RPi0 connecting all the sensors.

WS Analysis library documentation (v0.2)

The aim of this package is to provide fast and easy access and analysis to the Weather Station database. This package is located in the analysis directory, and it is compatible only with Python 3. Please follow the readme file for more information.

Directory Structure

project
├── 3D_Box
│   ├── Cap_v0_1stpart.skp
│   ├── Cap_v0_2dpart.skp
│   ├── ws_rpzero_noGPS_v1.skp
│   ├── ws_sensors_2d_half_v2.skp
│   └── ws_sensors_half_v2.skp
├── analysis
│   ├── arpa_station.json
│   ├── board.json
│   ├── example.py
│   ├── extract.py
│   ├── out.pdf
│   ├── requirements.txt
│   ├── ws_analysis
│   │   ├── __pycache__
│   │   │   └── ws_analysis.cpython-37.pyc
│   │   ├── rpt.txt
│   │   └── script_offset.py
│   ├── ws_analysis.md
│   ├── ws_analysis.pdf
│   ├── ws_analysis.py
│   └── ws_analysis.pyc
├── Dataset
│   ├── db_setup.html
│   ├── db_setup.md
│   ├── db_setup.pdf
│   ├── er_diagram.pdf
│   ├── mysql_insertion
│   │   ├── extract_to_file.py
│   │   ├── remove_duplicate.py
│   │   └── sql_ins.py
│   ├── SQL_Table
│   │   ├── create_db.sql
│   │   ├── create_measure_table.sql
│   │   └── load_data.sql
│   └── SQL_data
│      └── db_whole_data.sql.gz
├── PCB
│   └── WS_v2_output.tar.xz
├── readme.html
├── readme.md
├── readme.pdf
└── scripts
├── python
│   ├── csv
│   │   ├── arpa_retrieve.py
│   │   ├── filemerge.py
│   │   ├── gpx2geohash.py
│   │   ├── parse_csv.py
│   │   └── validation.py
│   └── mpu9250
│   └── gyro.py
└── README.md

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The data has been used to generate some of the figures presented in "Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements".

The measurement data belongs to the authors of "VOR base stations for indoor 802.11 positioning"

Instructions: 

Please read the file "README.txt"

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We present a novel, low-cost telerehabilitation system dedicated for bimanual training. The system captures the user’s movements with a Microsoft Kinect sensor and an inertial measurement unit (IMU). Herein, we deposit data we collected on a single, healthy subject who interacted with our system as described in our manuscript.

 

 

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Perceptual attributes or descriptors are used to describe odor impressions,with odor perception levels ranging from 0 to 100. We used 44 single-molecule substances to predict 9 olfactory perceptionsof pleasantness, namely, sweet, fruit, fish, garlic, grass, burnt, musky aroma and decayed.

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This dataset contains several acquired Lamb Waves from a thermoset matrix composite plate at different temperatures (-40ºC:+50ºC).

The dataset corresponds to the article with the same name.

Instructions: 

Use Matlab to load the files. Each file is named: Voltage[V]_Frequency[kHz]_Cycles_Temperature[K]_randomvalue.mat

Also included a .mat file with the coordinates of the transducers.

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Data shows results from experimentation into the photosensitive nature of functionalised and non-functionalised fungi

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