Open Access

Strain sensors based on the resistive principle have been developed and produced by inkjet printing of nanostructured materials. Miniaturized sensor structures with grid line widths of around 75 µm and overall dimensions of 6.7 mm × 6.4 mm were successfully realized on Polyimid foils. The influence of the surface properties and the silver nanoparticle ink on the printed strain gauges was examined. With adequate densification of the printed nanoparticle containing structures, gauge factors of around 3.9 or higher were achieved. Results of reliability tests under environmental stress showed the need for a protective coatings to ensure stable long-term behavior. With adequate coatings, stable sensors are possible under a wide range of environmental conditions, such as high temperature storage or thermal cycling.

The knowledge of the operating conditions in rolling bearings in technical applications offers many advantages, for example, to ensure a safe operation and to save resources and costs with the help of condition monitoring and predictive maintenance procedures. In many cases, it is difficult to implement sensors to measure the operating conditions of the rolling bearing, for reasons such as inaccessibility of the mounting position or non-compliance with installation space neutrality, which influences the sensor on the measuring point. Printed sensors using a digital deposition process, which can be used in very narrow design spaces, offer advantages in this respect. So far, these sensors have not been established in rolling bearings, so there is potential for technical application. This paper discusses the fundamental advantages and disadvantages as well as the challenges of the application, and it demonstrates the feasibility under isolated boundary conditions by applying a printed strain gauge sensor to the outer ring of a cylindrical roller bearing NU210 in an experimental setup to measure the strain under load. In this setup, the outer ring is deformed by 2 mm under an increasing radial load using a hydraulic press, and the strain is measured. Both a commercial reference sensor and a FE-simulation are used to validate the measurement. The results show that an implementation using printed sensors as a strain gauge works successfully. The resulting challenges, such as measuring strain gradients and printing on curved surfaces, are finally evaluated, and an outlook for further work is given.

Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed by using various methods. In this paper, the harmonics are reduced by using iterative learning control (ILC) and neural networks (NNs). This paper focuses on the startup behavior of the control system. The ILC can compensate well for the harmonics in operation at constant speed and constant current reference values, but needs multiple rotations to learn. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation and from the first rotation. The simulation model is based on flux and torque maps, depending on dq-currents and the electrical angle. The methods are also applied on the test bench and measurement results are presented.

Because of the absence of rare earth magnets, the reluctance synchronous machine has a more favourable sustainability balance compared to the permanent magnet synchronous machine with NdFeB magnets. Additional advantages include the absence of copper in the rotor and the ease of manufacture of the non modified rotor. A clear disadvantage of the reluctance synchronous machine is the relatively high torque ripple if no measures such as skewing are taken. Therefore, the machine is mainly used in applications such as fans where the torque ripple is not critical for the operation. The paper makes a contribution to addressing the problem of torque pulsation. For the reduction of torque harmonics, the usage of various rotor designs as well as asymmetric rotor geometry are presented. In addition, the aspect of torque harmonics, which is predicted by the finite element analysis is discussed and possibilities of torque ripple reduction for further investigations are shown. The description of a reluctance network of the machine is addressed with a semi-analytical approach for this purpose. Finally, the results of the preliminary calculation are validated by finite element analysis (FEA) and the quantities are verified with measurements on a device under test. The deviations between the methods are shown and discussed.

In this paper, the influence of temperatures up to 250 °C on core losses and magnetization demand of electrical steel sheets is illustrated. Therefore, measurements are made using an adopted high-temperature Epstein frame and a stator core as a ring probe. Three different electrical steel sheet grades, M330-50A, M530-50A and NO20-15, and a stator core are tested. Magnetic property measurements are performed at various frequencies from DC to 1.2 kHz with a defined sinusoidal waveform of magnetic polarization. This covers a wide range of electric motor applications from industrial drives to electric vehicle drives. With increasing temperature, magnetization demand rises significantly in medium polarization range. For specific core losses, a loss separation is performed using the Jordan approach, separating specific hysteresis and specific eddy current losses to determine the temperature influence on each loss component. The temperature behavior of the specific electrical resistivity of electrical steel sheets is measured and used to calculate the physical specific eddy current losses. After separation, specific hysteresis losses also show a significant temperature influence.