R&D personnel create smart motors without additional sensors

The research team led by the University of Saarland professor Matthias Nienhaus created a smart motor system by using the motor itself as a sensor to not only distinguish whether the system is still stable but also to communicate and interact with other motors. Can be effectively controlled.

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

Researchers develop smart motors without sensors The research team, led by the University of Saarland professor Matthias Nienhaus, is working to develop a new automatic monitoring motor that does not require sensors at all.

"We are currently developing an important new type of sensor: the motor itself," says Nienhaus. “The advantage of this new method is that engineers only need to collect information from the normal operation of the motor. Because there is no need to install any additional sensors, the path we use is very cost-effective. At present, we are looking at a more sophisticated approach. - Not only can we extract data from motors, but we can also use these data for motor control and monitoring and management procedures, and we are also working with our program partners to jointly improve the design and construction of micro motors so that they can produce the maximum amount of Operational information."

Just as doctors use blood tests to determine how to improve patient health, Nienhaus and his team used motor data to determine the health of the drive system. "We check the relationship between the measured data and the specific state of the motor, and how the specific test volume changes when the motor is not operating as expected," says Nienhaus.

For the research team, the data collected from the normal operation of the motor is extremely valuable; the more motor data it possesses, the more efficient the control of the motor. 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.

Research results will be fed to the system's brain - the microcontroller (MCU) for data processing. If some kind of signal change occurs, the MCU can immediately find a potential fault or error and respond accordingly. This "perceived" motor can be linked together via a networked operating system to form an integrated, composite system that opens up opportunities for maintenance, quality assurance and production. In addition, developers can also use their creativity to design a system where one of the motors fails and other motors automatically take over control at any time.

In order to collect information from the motor, Nienhaus and his research team carefully monitored the precise distribution of the magnetic field strength around the motor. When the current flows through the coil located in the outer ring of the permanent magnet of the spin, an electromagnetic field is generated. The researchers recorded how the magnetic field changed as the motor rotated. 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 controlled more effectively and more reliably detecting an error condition.

Nienhaus is currently testing a variety of different methods to determine the best way to extract data from the motor. The results of this study are part of the "MoSeS-Pro" plan for Modular Sensing Systems for Real-Time Process Control and Smart State Analysis. Partners include companies such as Bosch, Festo, Sensitec, Pollmeier, CANWAY, and Lenord, Bauer & Co. . 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).

(Original title: Researchers develop sensorless smart motors)

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