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 aim of this contribution is to identify and quantify the magnetic field parameter (MP) devices for charging electric vehicles (EVs). An EV is a mobile device. The EV remains a mobile device even when it is charging in a fixed charging stand. ICNIRP and SBM standards apply to stable devices. A magnetic field (MF) creates local gradient fields that change cyclically over time near the charging stations. The rotating vector MF is a specific parameter. An MF is evaluated by its strength and spatial changes. The triaxial fluxgate magnetometer VEMA-041 was used for the measurements. The MF was observed in the frequency range of 0–250 Hz, and the magnetic induction density was from T 2 × 10⁻⁹ T to 2 × 10⁻⁵ T, with a sensitivity of 1.7 nT. The MF analysis was performed within the time and frequency range. The rotating vector MF was identified at the measurement points. Measurements were realized for the charge under the following parameters: cables, 600 A; transformer, 250 kVA (22 kV/400 V); a cab-fixed charging stand, and an AC/DC charger in the EV. EV charging was performed with 6.6 kW of power and 43-kW fast charging. The measured results were satisfactory, according to the ICNIRP and SBM 2015 standard. The values measured at a distance of 1 m from the wall of the transformer were B_RMS < 2 µT. B_RMS values < 3 µT were measured in the space of the cable’s entry into the distribution box. EV values should not be assessed under this regulation. However, an EV is a mobile device. In the selected EV sample (a first-generation Nissan Leaf), a frequency of 10 Hz and its multiples were detected during charging. The frequencies were generated in an AC/DC charger in the EV. These frequencies reached B_RMS < 0.2 µT in the driver’s footwell. The maximum value of the MF rotating vector was B_total < 0.3 µT and was directed to the crew area of the EV. The AC/DC charger generated B_RMS = 0.95 µTin the driver’s footwell. It is necessary to look for new tools for evaluating MFs for EVs, such as the standards used for stable sources today. These standards should be based on dosimetric principles.

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Tools for quantitative risk assessment used in praxis are predominantly based on linear transformations. A parallel problem of current risk assessment methods is constant time and imperceptions of variability of threat over the course of time....