A sensor already developed by SAM for monitoring side channel pumps was adapted for operation on centrifugal pumps in the IoT.H2O project. The sensor consists of a Raspberry PI Zero and a circuit board developed at the electronics workshop of TU Kaiserslautern on which an accelerometer is mounted. The housing’s sensor was produced by 3D printing.
The operating principle of the sensor is based on a neural network. To train the neural network, operating data of the pump are recorded at different rotational speeds with a very narrow grating (speed and flow rate). The following are measured: speed, flow rate, pressures on suction and pressure side of the pump, torque and, through the pump sensor, the vibrations at the respective operating points. By training the neural network, a correlation between vibration and the pump’s operating point can be determined. After training, the operating point of the pump can be determined solely by measuring the vibration with the pump sensor. The installation of additional measurement devices (pressure transducer, MID, etc.) is no longer necessary.
This provides a very cost-effective measuring system. Data transmission from the pump sensor to the IoT node can take place via Bluetooth or the I2C interface.
To determine the optimal sensor position, three brackets were designed for mounting the sensors on the pump. This allows multiple vibration sensors to be mounted simultaneously at different positions on the pump. One sensor is located on the pump housing, one on the flange on the motor side and one on the flange of the discharge port.
Due to contact restrictions in the COVID19 pandemic, laboratory experiments could not be conducted in presence. For this reason, the technologies developed in IoT.H2O were used for the first time in the summer semester of 2021 to conduct virtual laboratory experiments. For this purpose, the students were given access to the dashboard created in the IoT platform for controlling the centrifugal pump for a certain period. This meant that the experiments could be carried out online. To avoid the risk of faulty operation and the need to supervise the test stand, a digital twin of the centrifugal pump was developed, which was operated via the dashboard of the IoT platform instead of the real pump. This digital twin realistically reproduces the operating behavior of a centrifugal pump and the drive motor.
The students were able to use it to determine the characteristic curve of the centrifugal pump, simulate different systems and investigate the differences between throttle and speed control.
In addition to avoiding the presence required to comply with contact restrictions, there are other advantages to the digital laboratory experiment. Up to now, the tests were always carried out in groups due to time constraints. As a rule, one or two students were then able to operate the test stand and record some measured values. The other participants had to watch. At the end of the experiment, all participants were provided with a complete data set of the measured values for the preparation of the laboratory report. Through the digital lab experiment, each participant can conduct the experiments themselves and record their own measurement data. This contributes to a deeper and better understanding of the operating behavior of centrifugal pumps and enables the students to analyze pumps operation in systems and to design them in an energy-efficient way. Another advantage is that by using a digital twin, other turbomachinery that are not available in the laboratory can also be studied in digital laboratory experiments. That way another experiment with a positive displacement pump has been developed.
Of course, the digital experiment does not replace the inspection of the test stand, which gives the students a better impression of the set-up of the test stand and the equipment used. Therefore, both concepts will be pursued in the future, also because of the very positive feedback from the students.
In addition to IoT-capable devices, remote procedure calls in Thingsboard can be used to execute any programme on computers connected to the internet. Thingsboard thus offers the possibility of combining data from different systems on distributed computers, such as measurement data or data from simulations, in a simple way. In the IoT.H2O project, for example, pipe network simulations will be carried out continuously in the future based on current measurement data. With a large number of sensors in the network, the pipeline network can thus also be continuously monitored by comparing simulation and measurement. Furthermore, software models for a virtual pump test bench and a virtual drinking water plant are to be developed using the example of the EWR Net AG plant in Worms.
After completing the tests for controlling a LED with Thingsboard successfully, devices based on an ESP32 microcontroller for controlling a throttle valve and a frequency converter at a pump test rig of the Institute for Fluid Mechanics and Turbomachinery was developed. With these devices it is now possible to operate the test rig through a Thingsboard Dashboard.
In addition to the visualisation and analysis of measurement data, Thingsboard also enables the control of devices via the so-called Remote Procedure Calls. This allows functions to be executed on other devices connected to the internet using the MQTT protocol and the results to be sent back to Thingsboard. To test the functionality, simple circuits were built with an ESP32 microcontroller and a LED. The LED can be switched on and off via Thingsboard and can also be dimmed when using the digital-to-analogue converter integrated in the EPS32.
In order to increase the robustness of the IoT nodes, circuit boards for the IoT nodes were developed in cooperation with the electronics workshop of the TU Kaiserslautern. The boards replace the breadboard that was used in the first prototype. The IoT node consists of a motherboard for the microprocessor and module boards with which the node can be adapted to use different sensors. We would like to thank the electronics workshop in particular for the great support!
In addition to the collection and visualisation of measurement data from drinking water facilities, individual components such as pumps or valves must be controlled. This is not possible with Grafana. For this reason, an installation of the IoT platform Thingsboard was put into operation. Thingsboard is also freely available. The measurement data transmitted via LoRaWAN is sent to the Thingsboard server using the MQTT Protocol and can be displayed in a dashboard.
To visualise the data transmitted by the LoRaWAN devices, the IoT dashboard Grafana was put into operation. The data is written with a Node-Red Flow from the LoRaWAN server into an InfluxDB database and then visualised with Grafana. For this purpose, a server was set up that is accessible to all project partners. This enables the visualisation of measurement data from the different locations of the project partners.
The first field test of the IoT system took place at the end of January. For this purpose, a gateway and two IoT nodes were installed in the Jockgrim waterworks of the administration union for Water Supply Germersheimer Südgruppe. Measured data can currently be transmitted from a deep well and a mains pump. At present, the gateway is in the waterworks building. We expressly thank for the great support from Mr. Friedmann and Mr. Justen!
An important task in the IoT.H2O project is the monitoring of pumps. For this purpose, an IoT node was developed, with which the pump delivery volume and the pressures in front of and behind the pump can be measured simultaneously. The IoT node is currently being tested on one of the pump test benches at TU Kaiserslautern.