Intelligent motor system that collects data without sensors

Engineers at the University of Saarland in Germany are working on an intelligent motor system that does not require an external sensor. 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.

The research team, led by Sarah University professor Matthias Nienhaus, created an intelligent motor system that uses the motor itself as a sensor to not only tell if the system is still stable, but also to communicate and interact with other motors. Can be effectively controlled.

In addition to letting the drive learn how to use the data, the researchers are currently working with the program partners to research and test a variety of different steps. The ultimate goal is to make the manufacturing process more cost-effective and flexible, enabling continuous monitoring of machinery and equipment for signs of failure or wear.

“We are currently developing an important new type of sensor: the motor body,” Nienhaus said. “The advantage of this new approach is that engineers only need to collect data from the normal operation of the motor. Because there is no need to install any additional sensors, the approach we take is very cost effective. Currently we are looking at a more elegant approach. —— Not only can we draw data from the motor, but also use this data for motor control and monitoring and management procedures. We are also working with our program partners to improve the design and construction of micromotors to maximize the amount of Operational information."

The research team, directed by Matthias Nienhaus, a professor at the University of Saarland, is working on a new automatic monitoring motor that requires no sensors at all.

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 specific state of the motor, and how the specific test volume changed when the motor was not operating as expected,” Nienhaus said.

For the research team, the data collected from the normal operation of the motor is extremely valuable; the more motor data it has, the more efficient the motor can be. Engineers analyze a large amount of motor data in order to find out some signal patterns that can be used to infer information about the current condition of the motor or to 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 of the study are fed into the brain of the system, the microcontroller (MCU), for processing the data. If a signal change occurs, the MCU can find potential faults or errors in real time and react accordingly. This “sensible” motor can be connected through a network operating system to form an integrated, integrated system that opens up more opportunities for repair, quality assurance and production. In addition, developers can also use their creativity to design a system in which one of the motors fails and the other motors automatically take over control at any time.

To collect data from the motor, Nienhaus and his team carefully monitored the exact distribution of the magnetic field strength near the motor. When a current flows through a coil located within the outer ring of the spin permanent magnet, an electromagnetic field is generated. The researchers recorded how the magnetic field changes as the motor rotates. These data can then be used to calculate the position of the rotor and to infer the state of the motor, allowing the motor to be more effectively controlled and more reliably detecting false states.

Nienhaus is currently testing a variety of different methods to determine the best way to retrieve data from the motor. The results of the study are part of the “Modular Sensing System for Real-Time Process Control and Intelligent State Analysis” (MoSeS-Pro) program, with partners including Bosch, Festo, Sensitec, Pollmeier, CANWAY and Lenord, Bauer & Co. the company. The research team is working to determine which motor speed ranges 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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