AGH University of Science and Technology Uses UbiBot WS1 Wi-Fi for Railway Turnout Weather and Rail Temperature Monitoring
Research Overview
| Paper Title | The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions |
| Publisher | MDPI |
| Journal | Sensors |
| Publish Time | August 2021 |
| Authors / Institutions | Arkadiusz Kampczyk and Katarzyna Dybeł; AGH University of Science and Technology, Poland |
| UbiBot Product | UbiBot WS1 Wi-Fi wireless temperature, humidity, and illumination logger with external UB-DT-P1 (DS18B20) temperature sensor |
| Data Collected | Ambient temperature, temperature inside the rail head, humidity, and ambient light |
| Sampling Frequency | Every hour |
| Research Period | January 21, 2020 to May 29, 2020 |
| Application Scenario | Railway turnout monitoring, digital twin data acquisition, Continuous Welded Rail temperature analysis, railway infrastructure diagnostics |
| Original Link | https://doi.org/10.3390/s21175757 |
Research Background: What Problem Did This Study Address?
Railway turnouts are critical components of railway infrastructure. They guide trains from one track to another and are exposed to complex mechanical loads, outdoor weather conditions, and frequent operational stress. Compared with regular track sections, turnouts are structurally more complicated and often require more intensive inspection and maintenance. Any abnormal condition in a turnout may affect railway safety, traffic efficiency, and maintenance cost.
The study focused on how digital twin technology can be applied to railway turnouts. A digital twin is not only a 3D representation of a physical asset; it also requires real-world data to continuously update and reflect the actual condition of the monitored object. For railway turnouts, this means that geometry, technical parameters, operational indicators, and environmental conditions should all be considered.
The researchers emphasized that weather-related data, especially rail temperature, should be included in the basic data structure of railway turnout digital twins. Steel rail temperature is affected by ambient temperature, sunlight, humidity, and seasonal changes. These environmental factors may influence the stress state of Continuous Welded Rail and may be relevant to the monitoring and diagnosis of railway infrastructure.
To support this research goal, the team developed a monitoring setup using a UbiBot WS1 Wi-Fi wireless data logger and an external UB-DT-P1 temperature sensor. The system collected real-world environmental data and rail-head temperature data over several months, providing a practical dataset for analyzing rail temperature changes and discussing their role in turnout monitoring and digital twin applications.
The Specific Role of UbiBot in the Study
In this study, UbiBot was used as a real-world data acquisition device. The research did not describe UbiBot as proving a scientific conclusion by itself. Instead, the researchers used UbiBot to collect environmental and rail temperature data from an actual railway-related monitoring setup, and then used those data to support further analysis.
The monitoring system was named Tszyn WS1 WiFi. It consisted of a UbiBot WS1 WiFi wireless logger, an external UB-DT-P1 temperature sensor, and a short section of S49, also known as 49E1, rail. The rail section was approximately 300 mm long. A measurement hole was prepared inside the crown of the rail, and the UB-DT-P1 probe was inserted into the rail head to measure the temperature inside the rail material.
The UbiBot WS1 WiFi device was deployed near the monitored railway turnout area, outside the structure gauge and parallel to the rail tracks. This placement allowed the system to monitor environmental conditions without interfering with railway operation.
The system collected four types of data: ambient temperature, temperature inside the rail head, humidity, and ambient light. The sampling frequency was once per hour. The monitoring period lasted from January 21, 2020 to May 29, 2020, covering winter and spring conditions. During this period, the system recorded 5002 data points.
The collected data were used in several ways. First, the rail-head temperature data were used to calculate temperature difference indicators, including the second temperature difference indicator TgCWRII. Second, ambient temperature, humidity, and light data were used to interpret the environmental context behind rail temperature changes. Third, the dataset provided an example of how continuous environmental monitoring can support the data layer of a railway turnout digital twin.
The role of UbiBot in this study can therefore be summarized as follows: it provided continuous, time-stamped, field-based environmental data that helped the researchers analyze how rail temperature changed under real outdoor conditions and how such data could be incorporated into railway turnout monitoring and digital twin systems.
Research Methods and Data Collection Approach
The researchers designed a measuring station called Tszyn WS1 WiFi. The station combined a UbiBot WS1 WiFi wireless data logger with an external UB-DT-P1 temperature sensor integrated into an S49 rail section.
The UB-DT-P1 sensor was installed inside the rail head to measure the internal temperature of the rail. The UbiBot WS1 WiFi logger recorded ambient temperature, humidity, and ambient light. Data synchronization was performed wirelessly through Wi-Fi and the UbiBot IoT Platform. The UbiBot App allowed the researchers to configure the device, manage observation cycles, check data remotely, and set alerts for values outside acceptable ranges.
The system also supported data export in CSV and PDF formats. This enabled the researchers to use the recorded data for further calculations and analysis.
The monitoring was conducted from January 21, 2020 to May 29, 2020. The device recorded data every hour. For temperature difference analysis, the researchers selected data from three fixed measurement times each day: morning at 7:00 a.m., noon at 1:00 p.m., and evening at 7:00 p.m.
The rail-head temperature values from these three daily measurement times were used to calculate the first and second rail temperature difference indicators. The second temperature difference indicator, TgCWRII, was used to describe changes in rail temperature across the selected measurement epochs.
Key Research Findings
The collected data showed that rail steel temperature was sensitive to changing atmospheric conditions. As the monitoring period moved from winter into spring, rail temperature, ambient temperature, humidity, and ambient light all changed significantly.
The researchers observed that rail temperature differences were often more noticeable during the afternoon. This suggested that daytime environmental conditions, especially sunlight and rising ambient temperature, could influence the internal temperature of the rail head.
By comparing rail temperature values at 7:00 a.m., 1:00 p.m., and 7:00 p.m., the researchers were able to calculate temperature difference indicators. These indicators helped describe how the rail temperature changed during a day and across the monitoring period.
The study also discussed the relationship between the second temperature difference indicator TgCWRII and the periodic average rail temperature. According to the researchers, combining these indicators may help estimate changes in the stress condition of Continuous Welded Rail. For example, large rail temperature fluctuations or unusual combinations of average rail temperature and temperature difference values may be relevant to infrastructure monitoring and maintenance analysis.
It is important to state this carefully: UbiBot did not independently determine railway failure or prove a risk condition. Instead, UbiBot collected the field data that the research team used to calculate indicators, compare temperature changes, and discuss how such data may support turnout monitoring and digital twin development.
What This Means for Railway Turnout Monitoring
This study highlights the importance of environmental data in railway turnout monitoring. Railway turnouts operate outdoors, where temperature, humidity, sunlight, and seasonal changes may affect rail behavior. If digital twin models only include static geometry or design information, they may not fully represent the actual operating environment.
By collecting real-time or periodic environmental data, infrastructure managers can better understand how railway turnouts and rail sections respond to changing weather conditions. Rail-head temperature is particularly important because the temperature of steel rails can differ from ambient air temperature.
The study suggests that environmental monitoring can become part of the basic data layer for railway turnout digital twins. When combined with geometry measurements, inspection records, and operational data, temperature and weather data may help create a more complete picture of turnout condition.
For railway maintenance, this means that monitoring systems can move beyond periodic manual inspections. Continuous or regular data acquisition can support long-term trend analysis, help identify unusual temperature patterns, and provide data for maintenance planning.
Application Value of UbiBot Devices
The UbiBot WS1 WiFi demonstrated several practical values in this research context.
First, it enabled continuous data collection in an outdoor railway-related monitoring environment. The device recorded ambient temperature, humidity, and light, while the external UB-DT-P1 sensor measured the temperature inside the rail head.
Second, it supported wireless data synchronization through Wi-Fi and the UbiBot IoT Platform. This allowed measurement data to be accessed remotely, which is useful for distributed infrastructure monitoring scenarios such as railways.
Third, the device provided local data storage. If the wireless connection was temporarily interrupted, the device could continue storing data and maintain data history. This is important for field monitoring, where network conditions may not always be stable.
Fourth, the system supported data export. The researchers could export data and use it for further analysis, including the calculation of temperature difference indicators.
Fifth, the device configuration could be adjusted through the UbiBot App. This made it possible to define observation cycles and alerts according to the needs of the monitoring task.
Overall, the application value of UbiBot in this study was its ability to provide structured, time-based, real-world environmental data for railway infrastructure analysis.
Extended Application Scenarios
The monitoring approach described in this study may be extended to several related scenarios.
One possible scenario is Continuous Welded Rail temperature monitoring. Since rail temperature can influence stress conditions, long-term temperature records may support maintenance analysis for welded rail sections.
Another scenario is railway turnout digital twin development. UbiBot devices can provide environmental data inputs that complement geometric, mechanical, and operational data in a digital twin system.
The same approach may also be used in marshalling yards and large turnout areas, where complex track layouts and frequent operations increase monitoring requirements.
Railway bridges, tunnel portals, and exposed track sections may also benefit from environmental monitoring. These locations may experience different sunlight, ventilation, humidity, and temperature conditions.
The system could also be considered for railway infrastructure in mining areas or regions affected by ground movement. In such locations, environmental data may be combined with structural and geometric monitoring data to support a broader infrastructure risk assessment.
FAQ
1. Which UbiBot product was used in the study?
The study used a UbiBot WS1 WiFi wireless temperature, humidity, and illumination logger with an external UB-DT-P1 temperature sensor.
2. What data did the UbiBot system collect?
The system collected ambient temperature, humidity, ambient light, and temperature inside the rail head.
3. Where was the sensor deployed?
The UB-DT-P1 temperature sensor was inserted into the crown of an S49, or 49E1, rail section. The UbiBot WS1 WiFi device was deployed near the railway turnout monitoring area, outside the railway structure gauge and parallel to the rail tracks.
4. What was the sampling frequency?
The device recorded data every hour.
5. How long did the monitoring last?
The monitoring period lasted from January 21, 2020 to May 29, 2020.
6. How were the collected data used?
The rail-head temperature data were used to calculate rail temperature difference indicators, including TgCWRII. Ambient temperature, humidity, and light data were used to understand the environmental conditions behind rail temperature changes.
7. Did UbiBot directly diagnose railway failure?
No. UbiBot was used as a data collection tool. The research team used the collected data for calculation, comparison, and analysis related to railway turnout monitoring and digital twin applications.
8. Why is rail-head temperature important?
Rail temperature can differ from ambient air temperature and may be affected by sunlight, seasonal changes, and material behavior. Measuring the temperature inside the rail head provides more direct information about the rail’s thermal condition.
9. Why is this relevant to digital twins?
A digital twin requires real-world data to reflect the actual condition of a physical asset. UbiBot’s environmental and rail temperature data can serve as part of the data input layer for railway turnout digital twin models.
10. Can this monitoring method be used beyond railway turnouts?
Yes. Similar monitoring setups may be used for Continuous Welded Rail, marshalling yards, railway bridges, tunnel entrances, exposed track sections, and other railway infrastructure monitoring scenarios.
Related Resources
Academic Research
See More >>
AGH University of Science and Technology Uses UbiBot WS1 Wi-Fi for Railway Turnout Weather and Rail Temperature Monitoring
Research Overview
| Paper Title | The Fundamental Approach of the Digital Twin Application in Railway Turnouts with Innovative Monitoring of Weather Conditions |
| Publisher | MDPI |
| Journal | Sensors |
| Publish Time | August 2021 |
| Authors / Institutions | Arkadiusz Kampczyk and Katarzyna Dybeł; AGH University of Science and Technology, Poland |
| UbiBot Product | UbiBot WS1 Wi-Fi wireless temperature, humidity, and illumination logger with external UB-DT-P1 (DS18B20) temperature sensor |
| Data Collected | Ambient temperature, temperature inside the rail head, humidity, and ambient light |
| Sampling Frequency | Every hour |
| Research Period | January 21, 2020 to May 29, 2020 |
| Application Scenario | Railway turnout monitoring, digital twin data acquisition, Continuous Welded Rail temperature analysis, railway infrastructure diagnostics |
| Original Link | https://doi.org/10.3390/s21175757 |
Research Background: What Problem Did This Study Address?
Railway turnouts are critical components of railway infrastructure. They guide trains from one track to another and are exposed to complex mechanical loads, outdoor weather conditions, and frequent operational stress. Compared with regular track sections, turnouts are structurally more complicated and often require more intensive inspection and maintenance. Any abnormal condition in a turnout may affect railway safety, traffic efficiency, and maintenance cost.
The study focused on how digital twin technology can be applied to railway turnouts. A digital twin is not only a 3D representation of a physical asset; it also requires real-world data to continuously update and reflect the actual condition of the monitored object. For railway turnouts, this means that geometry, technical parameters, operational indicators, and environmental conditions should all be considered.
The researchers emphasized that weather-related data, especially rail temperature, should be included in the basic data structure of railway turnout digital twins. Steel rail temperature is affected by ambient temperature, sunlight, humidity, and seasonal changes. These environmental factors may influence the stress state of Continuous Welded Rail and may be relevant to the monitoring and diagnosis of railway infrastructure.
To support this research goal, the team developed a monitoring setup using a UbiBot WS1 Wi-Fi wireless data logger and an external UB-DT-P1 temperature sensor. The system collected real-world environmental data and rail-head temperature data over several months, providing a practical dataset for analyzing rail temperature changes and discussing their role in turnout monitoring and digital twin applications.
The Specific Role of UbiBot in the Study
In this study, UbiBot was used as a real-world data acquisition device. The research did not describe UbiBot as proving a scientific conclusion by itself. Instead, the researchers used UbiBot to collect environmental and rail temperature data from an actual railway-related monitoring setup, and then used those data to support further analysis.
The monitoring system was named Tszyn WS1 WiFi. It consisted of a UbiBot WS1 WiFi wireless logger, an external UB-DT-P1 temperature sensor, and a short section of S49, also known as 49E1, rail. The rail section was approximately 300 mm long. A measurement hole was prepared inside the crown of the rail, and the UB-DT-P1 probe was inserted into the rail head to measure the temperature inside the rail material.
The UbiBot WS1 WiFi device was deployed near the monitored railway turnout area, outside the structure gauge and parallel to the rail tracks. This placement allowed the system to monitor environmental conditions without interfering with railway operation.
The system collected four types of data: ambient temperature, temperature inside the rail head, humidity, and ambient light. The sampling frequency was once per hour. The monitoring period lasted from January 21, 2020 to May 29, 2020, covering winter and spring conditions. During this period, the system recorded 5002 data points.
The collected data were used in several ways. First, the rail-head temperature data were used to calculate temperature difference indicators, including the second temperature difference indicator TgCWRII. Second, ambient temperature, humidity, and light data were used to interpret the environmental context behind rail temperature changes. Third, the dataset provided an example of how continuous environmental monitoring can support the data layer of a railway turnout digital twin.
The role of UbiBot in this study can therefore be summarized as follows: it provided continuous, time-stamped, field-based environmental data that helped the researchers analyze how rail temperature changed under real outdoor conditions and how such data could be incorporated into railway turnout monitoring and digital twin systems.
Research Methods and Data Collection Approach
The researchers designed a measuring station called Tszyn WS1 WiFi. The station combined a UbiBot WS1 WiFi wireless data logger with an external UB-DT-P1 temperature sensor integrated into an S49 rail section.
The UB-DT-P1 sensor was installed inside the rail head to measure the internal temperature of the rail. The UbiBot WS1 WiFi logger recorded ambient temperature, humidity, and ambient light. Data synchronization was performed wirelessly through Wi-Fi and the UbiBot IoT Platform. The UbiBot App allowed the researchers to configure the device, manage observation cycles, check data remotely, and set alerts for values outside acceptable ranges.
The system also supported data export in CSV and PDF formats. This enabled the researchers to use the recorded data for further calculations and analysis.
The monitoring was conducted from January 21, 2020 to May 29, 2020. The device recorded data every hour. For temperature difference analysis, the researchers selected data from three fixed measurement times each day: morning at 7:00 a.m., noon at 1:00 p.m., and evening at 7:00 p.m.
The rail-head temperature values from these three daily measurement times were used to calculate the first and second rail temperature difference indicators. The second temperature difference indicator, TgCWRII, was used to describe changes in rail temperature across the selected measurement epochs.
Key Research Findings
The collected data showed that rail steel temperature was sensitive to changing atmospheric conditions. As the monitoring period moved from winter into spring, rail temperature, ambient temperature, humidity, and ambient light all changed significantly.
The researchers observed that rail temperature differences were often more noticeable during the afternoon. This suggested that daytime environmental conditions, especially sunlight and rising ambient temperature, could influence the internal temperature of the rail head.
By comparing rail temperature values at 7:00 a.m., 1:00 p.m., and 7:00 p.m., the researchers were able to calculate temperature difference indicators. These indicators helped describe how the rail temperature changed during a day and across the monitoring period.
The study also discussed the relationship between the second temperature difference indicator TgCWRII and the periodic average rail temperature. According to the researchers, combining these indicators may help estimate changes in the stress condition of Continuous Welded Rail. For example, large rail temperature fluctuations or unusual combinations of average rail temperature and temperature difference values may be relevant to infrastructure monitoring and maintenance analysis.
It is important to state this carefully: UbiBot did not independently determine railway failure or prove a risk condition. Instead, UbiBot collected the field data that the research team used to calculate indicators, compare temperature changes, and discuss how such data may support turnout monitoring and digital twin development.
What This Means for Railway Turnout Monitoring
This study highlights the importance of environmental data in railway turnout monitoring. Railway turnouts operate outdoors, where temperature, humidity, sunlight, and seasonal changes may affect rail behavior. If digital twin models only include static geometry or design information, they may not fully represent the actual operating environment.
By collecting real-time or periodic environmental data, infrastructure managers can better understand how railway turnouts and rail sections respond to changing weather conditions. Rail-head temperature is particularly important because the temperature of steel rails can differ from ambient air temperature.
The study suggests that environmental monitoring can become part of the basic data layer for railway turnout digital twins. When combined with geometry measurements, inspection records, and operational data, temperature and weather data may help create a more complete picture of turnout condition.
For railway maintenance, this means that monitoring systems can move beyond periodic manual inspections. Continuous or regular data acquisition can support long-term trend analysis, help identify unusual temperature patterns, and provide data for maintenance planning.
Application Value of UbiBot Devices
The UbiBot WS1 WiFi demonstrated several practical values in this research context.
First, it enabled continuous data collection in an outdoor railway-related monitoring environment. The device recorded ambient temperature, humidity, and light, while the external UB-DT-P1 sensor measured the temperature inside the rail head.
Second, it supported wireless data synchronization through Wi-Fi and the UbiBot IoT Platform. This allowed measurement data to be accessed remotely, which is useful for distributed infrastructure monitoring scenarios such as railways.
Third, the device provided local data storage. If the wireless connection was temporarily interrupted, the device could continue storing data and maintain data history. This is important for field monitoring, where network conditions may not always be stable.
Fourth, the system supported data export. The researchers could export data and use it for further analysis, including the calculation of temperature difference indicators.
Fifth, the device configuration could be adjusted through the UbiBot App. This made it possible to define observation cycles and alerts according to the needs of the monitoring task.
Overall, the application value of UbiBot in this study was its ability to provide structured, time-based, real-world environmental data for railway infrastructure analysis.
Extended Application Scenarios
The monitoring approach described in this study may be extended to several related scenarios.
One possible scenario is Continuous Welded Rail temperature monitoring. Since rail temperature can influence stress conditions, long-term temperature records may support maintenance analysis for welded rail sections.
Another scenario is railway turnout digital twin development. UbiBot devices can provide environmental data inputs that complement geometric, mechanical, and operational data in a digital twin system.
The same approach may also be used in marshalling yards and large turnout areas, where complex track layouts and frequent operations increase monitoring requirements.
Railway bridges, tunnel portals, and exposed track sections may also benefit from environmental monitoring. These locations may experience different sunlight, ventilation, humidity, and temperature conditions.
The system could also be considered for railway infrastructure in mining areas or regions affected by ground movement. In such locations, environmental data may be combined with structural and geometric monitoring data to support a broader infrastructure risk assessment.
FAQ
1. Which UbiBot product was used in the study?
The study used a UbiBot WS1 WiFi wireless temperature, humidity, and illumination logger with an external UB-DT-P1 temperature sensor.
2. What data did the UbiBot system collect?
The system collected ambient temperature, humidity, ambient light, and temperature inside the rail head.
3. Where was the sensor deployed?
The UB-DT-P1 temperature sensor was inserted into the crown of an S49, or 49E1, rail section. The UbiBot WS1 WiFi device was deployed near the railway turnout monitoring area, outside the railway structure gauge and parallel to the rail tracks.
4. What was the sampling frequency?
The device recorded data every hour.
5. How long did the monitoring last?
The monitoring period lasted from January 21, 2020 to May 29, 2020.
6. How were the collected data used?
The rail-head temperature data were used to calculate rail temperature difference indicators, including TgCWRII. Ambient temperature, humidity, and light data were used to understand the environmental conditions behind rail temperature changes.
7. Did UbiBot directly diagnose railway failure?
No. UbiBot was used as a data collection tool. The research team used the collected data for calculation, comparison, and analysis related to railway turnout monitoring and digital twin applications.
8. Why is rail-head temperature important?
Rail temperature can differ from ambient air temperature and may be affected by sunlight, seasonal changes, and material behavior. Measuring the temperature inside the rail head provides more direct information about the rail’s thermal condition.
9. Why is this relevant to digital twins?
A digital twin requires real-world data to reflect the actual condition of a physical asset. UbiBot’s environmental and rail temperature data can serve as part of the data input layer for railway turnout digital twin models.
10. Can this monitoring method be used beyond railway turnouts?
Yes. Similar monitoring setups may be used for Continuous Welded Rail, marshalling yards, railway bridges, tunnel entrances, exposed track sections, and other railway infrastructure monitoring scenarios.
分享
.svg)
Related Resources
