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Saarbrücken engineers developing networked self-analyzing electric motors

Engineers from Saarland University are developing intelligent motor systems that function without the need for additional sensors. By essentially transforming the motor itself into a sensor, the team led by Professor Matthias Nienhaus is creating smart motors that can tell whether they are still running smoothly, can communicate and interact with other motors and can be efficiently controlled.

By using data collected from the motor while it is operating, the researchers are able to calculate quantities that in other systems would need to be measured by additional sensors. Further, they are teaching the drive how to make use of this knowledge.

We’re developing an important new type of sensor: the motor itself. 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. 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.

—Professor Nienhaus

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 motor data to identify those signal patterns that can be used to infer something about the current status of the motor or to flag up 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.

The results are fed into a microcontroller. 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.

In order to gather data from the motor, Nienhaus and his team carefully monitor the precise distribution of the magnetic field strength in the motor. An electromagnetic field is generated when electric current flows through the coils located within the outer ring of rotating permanent magnets. The researchers record how this magnetic field changes when the motor rotates. This data can then be used to compute the position of the rotor and to draw other inferences about the status of the motor, which allows the motor to be controlled efficiently and error states to be detected reliably.

Nienhaus is currently testing a number of different methodologies to determine those best suited to acquiring data from the motor. This work is being carried out as part of the project “Modular sensor systems for real-time process control and smart state monitoring” (MoSeS-Pro).

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 MoSeS-Pro project is being funded by the Federal Ministry of Education and Research (BMBF).

The goal of the MoSeS-Pro project is to develop a suite of hardware and software modules with which it will be easier to develop sensor systems for monitoring and controlling drives and positioning systems, paving the way for fast and precise manufacturing processes that can be monitored and adjusted in real time.

The project is being carried with the support of the associated partners Festo AG (Rohrbach plant) and Bosch Rexroth AG (Homburg plant) and the direct project partners Sensitec GmbH, Lenord, Bauer & Co. GmbH, ESR Dipl.-Ing. Pollmeier GmbH and CANWAY Technology GmbH. In addition to ZeMA, research partners in the MoSeS-Pro project are the Fraunhofer Institute for Microelectronic Circuits and Systems (IMS) and the Department of Integrated Sensor Systems at Kaiserslautern University of Technology.

The research work has received financial support totalling €3.1 million (US$3.5 million) as part of the BMBF funding programme “Sensor-based electronic systems for Industry 4.0 applications (SElekt I4.0)”, which is being managed by the project management agency VDI/VDE-IT. Around €540,000 (US$603,000) in funding has been allocated to Saarland University.

The researchers are currently working with project partners to study and test a number of different procedural methods. The ultimate goal is to make manufacturing processes more cost-effective and flexible and to enable machinery and equipment to be continuously monitored for faults or signs of wear.

The project will be on show at Hannover Messe from 25-29 April, where the team will be exhibiting at the Saarland Research and Innovation Stand in Hall 2, Stand B46.



So obvious really,
commercial diesel engines have had high levels of logging and in many cases both maritime and road going engines have real time monitoring telemetry with mostly data centre ability to adjust operating strategy.
They haven't got quite the same self intelligence and strategy modifying strategy as described here but de rating, limp home and fault, wear and in some cases detailed predictive repair preparation can enable the base or control room to have parts personnel etc ready for when the engine arrives at the repair shop or even if stranded.
No mechanic need be present to perform the diagnosis.

I still believe this style of waveform monitoring will be developed for autonomous smart(er) grid status monitoring by everything from consumer white goods to ev charging.

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