“We’re developing an important new type of sensor: the motor itself,” said Professor Matthias Nienhaus. “We’re looking at ways of extracting data from the motor and of using this data for motor control, monitoring and managing processes. We are also working with project partners on improving the design and construction of miniature motors that yield the greatest possible quantity of operational information.”
In order to gather data, the team monitors the precise distribution of the motor’s magnetic field streng and records how this magnetic field changes when the motor rotates. This data can then be used to compute the rotor’s position and to draw inferences about motor status. This, says the team, allows the motor to be controlled efficiently and error states to be detected reliably. The team is developing mathematical models that simulate the various motor states, fault levels and degrees of wear.
Collected data isfed into an MCU. If a certain signal changes, the MCU can identify the underlying fault or error and respond accordingly. These ‘sentient’ motors can also be linked via a network operating system and the team say it is conceivable that a system could be designed in which one motor takes over automatically if another fails.
Prof Nienhaus is currently testing a number of data acquisition methodologies and his team is working with partners to study and test a number of approaches, with the goal of making manufacturing processes more cost effective and flexible and to enable machinery and equipment to be monitored continuously for faults or signs of wear.