Magnetic position and speed sensors are rugged and durable. While DC magnetic sensors use permanent magnets as a field source and usually have only mm or cm range, inductive sensors use electromagnetic induction and they may work up to a distance of 20 m. Eddy current inductive sensors equipped with magnetoresistive sensors instead of inductive coils can operate at low frequencies, allowing detection through a conductive wall. In this paper, we make an overview of existing systems and we present new results in eddy current velocity and position measurements. We also present several types of inductive position sensors developed in our laboratories for industrial applications in pneumatic and hydraulic cylinders, underground drilling, large mining machines, and for detecting ferromagnetic objects on conveyors. While the most precise inductive position sensors have a resolution of 10 nm and linearity of 0.2%, precision requirements on the industrial sensors which we develop are less demanding, but they should have large working distance and large resistance to environmental conditions and interference.

The rapid development of industry results in large energy consumption and a negative impact on the environment. Pollution of the environment caused by conventional energy sources such as petrol leads to increased demand for propulsion systems with higher efficiency and capable of energy-saving and emission reduction. The usage of hybrid technology is expected to improve energy conversion efficiency, reduce energy consumption and environmental pollution. In this paper, the simulation platform for the hybrid unmanned aerial vehicle (UAV) has been built by establishing the subsystem models of the UAV power system. Under the two chosen working conditions, the conventional cruise flight mission and the terrain tracking mission, the power tracking control and Q-Learning method have been used to design the energy management controller for the hybrid UAV. The fuel consumption and pollutant emissions under each working condition were calculated. The results show that the hybrid system can improve the efficiency of the UAV system, reduce the fuel consumption of the UAV, and so reduce the emissions of CO2, NOx, and other pollutants. This contributes to improving of environmental quality, energy-saving, and emission reduction, thereby contributing to the sustainable development of aviation.

Unmanned aerial vehicles (UAVs) are used nowadays in a wide range of applications, including monitoring, mapping, or surveying tasks, involving magnetic field mapping, mainly for geological and geophysical purposes. However, thanks to the integration of ultrasound-aided navigation used for indoor UAV flight planning and development in sensorics, the acquired magnetic field images can be further used, for example, to enhance indoor UAV navigation based on the physical quantities of the image or for the identification of risk areas in manufacturing or industrial halls, where workers can be exposed to high values of electromagnetic fields. The knowledge of the spatial distribution of magnetic fields can also provide valuable information from the perspective of the technical cleanliness. This paper presents results achieved with the original fluxgate magnetometer developed and specially modified for integration on the UAV. Since the magnetometer had a wider frequency range of measurement, up to 250 Hz, the DC (Direct Current) magnetic field and low frequency industrial components could be evaluated. From the obtained data, 3D magnetic field images using spline interpolation algorithms written in the Python programming language were created. The visualization of the measured magnetic field in the 3D plots offer an innovative view of the spatial distribution of the magnetic field in the area of interest.

The magnetic field created by technical devices is a source of information. This information could be used in contactless diagnostics and predictive maintenance or for resolving problems along with standard NDT (nondestructive testing) methods, especially if we consider large, slow-speed devices, such as electromotors, transmissions, or generators. Identification of causalities of device failure processes with near magnetic field is one of the suitable NDT methods improving sustainability of systems. The measurements presented in the article were performed with the VEMA 04 fluxgate vector magnetometer with the DC-250 Hz bandwidth and 2 nT sensitivity. Postprocessing of the results was performed in the means of standard methods of discrete Fourier Transform, spectrogram creation and Wavelet Transform. The article presents data gathered during the measurement of a pair of extraction fans with power of 140 kW each and maximum revolutions up to 740 rev/min controlled by frequency converters and a single semi-Kaplan water power plant with 400 kW peak power at 1005 rev/min maximum generator speed. The measurements were performed before and after repairs of one of the ventilators in the ventilation system at 60% and 100% of maximal output power. The rotating magnetic fields of the fan electromotor stator, fan rotor revolutions, rotor slip frequency and ball-bearing frequencies were identified in frequency spectrums in the distance of 700 mm from fan electromotor axis in both cases. During the measurements on the semi-Kaplan turbine, the changes in states of mechanical and electrical components of the machine were monitored in the magnetic fields with increase of the power in the range of 0–95%, before and after phasing to the electrical grid. Standard processing methods, Discrete Fourier Transform, spectrograms and Discrete Wavelet Transform were used. In the spectrograms of the measured magnetic fields, the 1st–4th harmonics of the turbine shaft, generator shaft and also their side frequencies were identified. Significant changes of magnetic fields in time were identified in the area of 60–95% power. With the help of the Wavelet, transform intervals were identified where it is desirable to operate the turbine. The analyses of magnetic fields measurements performed on the power plant were compared with vibro-diagnostic principles.

The development of non-destructive methods for material testing and diagnostics has been, in the last few decades, focused mainly on optical, infrared, thermography, ultrasonic, acoustic or X-ray principles. This article deals with the possibility of adaptation of magnetic sensors for the diagnostics of aircraft structures. The developed sensors are based on the enhanced induction method, allowing contactless diagnostics of the material structure. In the role of the sensing element, amorphous magnetic microwires were used. Thanks to their dimensions, microwires can either be placed on the material surface or be embedded directly into the composite material without structural violations. In the article, the measurement principles of the developed microwire-based tensile stress sensors, together with the experimental measurements with the sensors originally tested in the aircraft wing, are presented.

This article deals with the design and creation of a tensometric measuring system to measure the parameters of an unmanned aerial vehicle (UAV) of the quadcopter type. The system was designed to measure the total UAV thrust and the thrust of its individual motors. The distribution of forces from the UAV motors and their transmission to the sensors was ensured by a specially designed construction, for which the mechanical stresses were simulated and analysed for different modes of the UAV flight. The thrust measurement was performed by four pairs of strain gauges. A measurement system designed in this way and the measured parameters of the UAV can be used for tuning the flight control algorithms applied in the autopilot.