20 April 2016
“That makes our approach very cost-effective as there’s no need to install any additional sensors. We’re looking at elegant ways of extracting data from the motor and of using this data for motor control and for monitoring and managing processes,” explains Nienhaus. Just like a doctor uses blood test data to draw conclusions about the health patient, Nienhaus and his team use motor data to determine the health of a drive system. “We examine how our measured data correlates with specific motor states and how specific measured quantities change when the motor is not operating as it should,” says Nienhaus.
Huge amount of motor data
Gathering data from the motor while it is operating normally is particularly valuable for the research team; the more motor data they have, the more efficiently they can control the motor. They analyse the huge amount of motor data in order to identify those signal patterns that can be used to infer something about the current status of the motor or to flag up changes arizing from a malfunction or from wear.
The team is developing mathematical models that simulate the various motor states, fault levels and degrees of wear. The results are fed into a microcontroller, the brain of the system in which the data are processed. If a certain signal changes, the controller can identify the underlying fault or error and respond accordingly. These “sentient” motors can be linked together via a network operating system to form an integrated complex that open up numerous opportunities in the fields of maintenance, quality assurance and production. It is also conceivable that a system could be designed in which one motor automatically takes over if one of the other motors fails. The project will be on show at Hannover Messe, where the team will be exhibiting at the Saarland Research and Innovation Stand in Hall 2, Stand B46.
A networked self-analyzing electric motor
The drive systems specialist Professor Matthias Nienhaus from Saarland University is working on developing a new kind of self-monitoring motor – one that doesn’t need sensors. “We’re developing an important new type of sensor: the motor itself,” says Nienhaus. The advantage of this new approach is that the engineers simply collect data that is available from the normal operation of the motor.
 Photo: Oliver DietzeMotor condition monitoring without sensors: By transforming the motor itself into a sensor, the team led by Professor Matthias Nienhaus are creating smart motors. |
Huge amount of motor data
Gathering data from the motor while it is operating normally is particularly valuable for the research team; the more motor data they have, the more efficiently they can control the motor. They analyse the huge amount of motor data in order to identify those signal patterns that can be used to infer something about the current status of the motor or to flag up changes arizing from a malfunction or from wear.
The team is developing mathematical models that simulate the various motor states, fault levels and degrees of wear. The results are fed into a microcontroller, the brain of the system in which the data are processed. If a certain signal changes, the controller can identify the underlying fault or error and respond accordingly. These “sentient” motors can be linked together via a network operating system to form an integrated complex that open up numerous opportunities in the fields of maintenance, quality assurance and production. It is also conceivable that a system could be designed in which one motor automatically takes over if one of the other motors fails. The project will be on show at Hannover Messe, where the team will be exhibiting at the Saarland Research and Innovation Stand in Hall 2, Stand B46.
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