Saarland self-monitoring motors won’t need additional sensors
March 23, 2016 By Anthony Capkun
March 23, 2016 – A team of engineers from Saarland University are developing intelligent motor systems that reveal their health without requiring additional sensors by, essentially, transforming the motors themselves into sensors.
“We’re developing an important new type of sensor: the motor itself,” said prof. Matthias Nienhaus. Engineers simply collect data that is available from the normal operation of the motor. “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.”
Led by Nienhaus, the team is creating smart motors that can tell whether they are running smoothly; can communicate and interact with other motors; and can be efficiently controlled. By simply using data collected from the motor while it is operating, the researchers are able to make calculations that, in other systems, would require additional sensors.
Just like a doctor uses blood test data to draw conclusions about a patient’s health, 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,” Nienhaus explained.
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. The engineers analyze the data to identify signal patterns that can be used to infer something about the current status of the motor, or to flag changes arising from a malfunction or from wear. The team is developing mathematical models that simulate the various motor states, fault levels and degrees of wear.
To gather data, Nienhaus and his team monitor the precise distribution of the magnetic field strength in the motor. They record how this magnetic field changes when the motor rotates. This data can then be used to compute the rotor’s position and draw other inferences about the motor’s status.
The results are fed into a microcontroller—the brain of the system. When 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 opens up opportunities in the fields of maintenance, quality assurance and production, say researchers. It is also conceivable that a system could be designed in which one motor automatically takes over should one of the others fail.
Nienhaus is currently testing a number of different methodologies to determine those best suited to acquiring motor data. The research team is looking to identify which motor speed range generates the best data and which type of motor is best suited for this type of application.
The team is working with project partners to study a variety of procedural methods with the ultimate goal of making manufacturing processes more cost-effective and flexible, and enabling machinery and equipment to continuously monitor itself for faults or signs of wear.
PHOTO: By transforming the motor itself into a sensor, the team led by prof. Matthias Nienhaus are creating smart motors. Photo Oliver Dietze.
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