Calibration of Magnetometer for Small Satellites Using Neural Network

Kliment, Tomas; Praslička, Dušan; Lipovský, Pavol; Draganová, Katarína; Zavodsky, Ondrej

doi:10.12693/aphyspola.131.1129

Cited by 9 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many calibration methods that can be used for magnetometer calibration [ 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. In our case, the magnetometer was calibrated using the neural network algorithm [ 88 ] and noise analysis methodology [ 89 ] developed at our department, which was also applied for the onboard satellite magnetometer calibration, described in detail in [ 88 ], with 500 calibration points of the total magnetic field of 60 μT generated with equal distribution on a virtual sphere surface. The achieved sensitivity was approximately 3.5 nT/LSB for all channels; only small differences among the channels were observed, and the internal counters in the FPGA were clocked with the 200 MHz signal; thus, the LSB was equal to 5 ns.…”

Section: Methodsmentioning

confidence: 99%

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Lipovský

Draganová

Novotňák

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Lipovský

Draganová

Novotňák

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Many measurements were made [17,27], mainly in the area of non-contact measurements, confirming the suitability of the method and the VEMA-041 magnetometer. The VEMA-041 is a purpose-built measuring device designed for the measurement of MFs [30][31][32] for the needs of military aviation. Compared with industrial measuring devices such as those from NARDA [26], the VEMA-041 is more accurate.…”

Section: Experimental Methods and Experimental Equipmentmentioning

confidence: 99%

Magnetic Fields of Devices during Electric Vehicle Charging: A Slovak Case Study

et al. 2021

View full text Add to dashboard Cite

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 BRMS < 2 µT. BRMS 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 BRMS < 0.2 µT in the driver’s footwell. The maximum value of the MF rotating vector was Btota l< 0.3 µT and was directed to the crew area of the EV. The AC/DC charger generated BRMS = 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.

show abstract

“…where t + and t − time intervals are relaxation times from corresponding saturations, k and q calibration constants are evaluated for each channel with the calibration process using the neural network [64]. Due to small differences among the sensors, each channel has its own constants in the memory of the microcontroller unit that provides also communication interface to the PC connected to the magnetometer.…”

Section: Measurement Devicementioning

confidence: 99%

Low-Frequency Magnetic Fields in Diagnostics of Low-Speed Electrical and Mechanical Systems

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Calibration of Magnetometer for Small Satellites Using Neural Network

Cited by 9 publications

References 6 publications

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Indoor Mapping of Magnetic Fields Using UAV Equipped with Fluxgate Magnetometer

Magnetic Fields of Devices during Electric Vehicle Charging: A Slovak Case Study

Low-Frequency Magnetic Fields in Diagnostics of Low-Speed Electrical and Mechanical Systems

Contact Info

Product

Resources

About