Engineers at the University of Saarland in Germany are working on an intelligent motor system that does not require additional sensors. The system collects data while the motor is running and calculates parameters - in other systems, this part of the task usually has to be measured by adding more sensors.
A team led by Matthias Nienhaus, a professor at the University of Saarland, uses the motor itself as a sensor to create an intelligent motor system that not only identifies whether the system is still running smoothly, but also communicates and interacts with other motors and Can effectively control.
In addition to enabling drivers to learn how to leverage data, researchers are currently working with the program's partners on a variety of different approaches to testing and testing. The ultimate goal is to make the manufacturing process more cost-effective and more flexible, thus enabling continuous monitoring of machinery and equipment for signs of failure or wear.
"We are currently developing an important new sensor: the motor itself," Nienhaus said. "The advantage of this new approach is that engineers only need to gather data from the normal operation of the motor, because we do not need to install any additional sensors, making our approach is very cost-effective way.We are now looking at a more elegant way - Capable of not only retrieving data from the motor but also using the data for motor control and monitoring and management procedures.We are also working with our program partners to improve the design and construction of the miniature motor so that it produces the largest amount of Operation information. "
The research team led by Matthias Nienhaus, professor at the University of Saarland, is working on a new automatic monitoring motor that does not require the use of sensors
Just as doctors use blood test data to make conclusions about how to improve patient health, Nienhaus and his research team use motor data to determine the health of the drive system. "We checked the relationship between the measured data and the motor-specific status and how the specific test quantity changes when the motor is not operating as expected," said Nienhaus.
For the research team, the data collected from the normal operation of the motor is of great value; the more data the motor has, the more efficient the control of the motor. Engineers analyze a large amount of motor data in order to find some signal patterns that can be used to infer information about the current state of the motor or indicate changes due to wear or failure. The research team is developing mathematical models to simulate various motor states, fault conditions, and wear levels.
The results will be fed to the system's brain - the microcontroller (MCU) for data processing. In the event of a signal change, the MCU can find a potential fault or error in real time and respond accordingly. This "sensory" motor can be connected via a networked operating system to create an integrated, composite system that opens up even more opportunities for service, quality assurance and manufacturing. In addition, developers can be creative and devise a system where one of the motors fails and other motors take over the control at any time.
In order to collect data from the motor, Nienhaus and his research team carefully monitored the exact distribution of the magnetic field strength in the vicinity of the motor. When a current flows through the coil in the permanent magnet outer ring, an electromagnetic field is generated. Researchers record how the magnetic field changes as the motor rotates. These data can then be used to calculate the position of the rotor and infer the state of the motor, allowing the motor to be more efficiently controlled and more reliably detect erroneous conditions.
Nienhaus is currently testing a variety of different ways to determine the best way to retrieve data from the motor. The results are part of the MoSeS-Pro Program for Modular Sensing Systems (Bosch, Festo, Sensitec, Pollmeier, CANWAY, Lenord, Bauer & Co., etc.) the company. The research team is working to determine which motor speeds produce the best data and which motor is best suited for this type of application. The MoSe-Pro program is sponsored by the Federal Ministry of Education and Research (BMBF).
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